Urbanization represents one of the most important structural transformations in human development since the era of industrialization. By the year 2050, it is anticipated that over two-thirds of the global population will reside in urban regions. While this shift holds the promise of economic advancement and modernization, it also presents intricate challenges, especially for countries in the Global South. These nations encounter significant problems such as congestion, traffic management issues, insufficient public services, shortages of resources, and environmental decline. Tackling these challenges necessitates creative solutions, with the concept of smart cities emerging as a promising approach. Leveraging advanced technologies, smart cities strive to enhance urban management and improve residents' quality of life. Recent research highlights that integrating digital platforms with traditional in-person education leads to better results in technical fields that require both conceptual comprehension and direct problem-solving abilities, such as accounting and finance (Awashreh, Al Ghunaimi & Hassiba, 2025). Within this technological framework, Artificial Intelligence (AI) is pivotal due to its capacity to process enormous quantities of data, recognize patterns, and optimize urban systems in real-time (Ritchie, Samborska & Roser, 2024). Despite the swift growth of smart city projects, there is still a notable gap in understanding how AI deployment intersects with the local socio-economic, political, and ethical landscapes of the Global South. This disparity is even more pronounced due to the absence of resilience and preparedness in entrepreneurial ecosystems within emerging markets, particularly during crises like the COVID-19 pandemic, which restricts their ability to adapt and limits technological readiness for intelligent urban transformations (Al Ghunaimi et al., 2024). In the case of Oman, this disparity became particularly evident as numerous countries are swiftly advancing in digitalization in accordance with their national strategic goals, while simultaneously encountering challenges in the adoption by SMEs and in upgrading infrastructure (Al Ghunaimi et al., 2025). This gap is further amplified by unequal participation of Global South scholars and policymakers in global AI ethics discussions, which risks reinforcing marginalization and failing to capture region-specific challenges (Roche, Lewis, & Wall, 2021). This research aims to directly close this gap.

AI holds transformative possibilities for addressing urban issues, particularly in the Global South. It improves traffic management by processing real-time data, which results in enhanced transportation efficiency and reduced traffic congestion. Fintech and AI have shown to be revolutionary in improving financial governance and sustainability outcomes, providing frameworks that may encourage innovation in public services within smart cities (Almaqtari et al., 2025). Additionally, it boosts public safety through predictive analytics, allowing for more effective allocation of law enforcement resources. Furthermore, AI aids in the sustainable management of critical resources like energy and water, which is extremely beneficial in areas facing scarcity and infrastructure challenges (Herath & Mittal, 2022). However, despite its numerous advantages, the implementation of AI in smart cities faces various challenges. Contemporary research suggests that the deployment of AI is deeply intertwined with global power dynamics, particularly in developing countries. Concepts like data colonialism reveal how data originating from the Global South is increasingly extracted, processed, and controlled by entities in the Global North, thereby creating new layers of dependency and exploitation (Couldry & Mejias, 2019; Taylor & Broeders, 2015). Moreover, algorithmic governance frameworks highlight how opaque AI systems tend to function without adequate local oversight, transparency, or accountability, raising issues related to digital sovereignty and democratic governance (Yeung, 2017). These dynamics have the potential to reinforce global dependence, curtail local autonomy in decision-making processes, and worsen inequalities in data governance and exploitation. Key concerns include data privacy, cybersecurity, and the digital divide, which can further intensify inequalities in developing countries (Almeida, 2023). Additionally, many areas lack the critical digital infrastructure and skilled labour necessary for the effective implementation and utilization of AI, leading to questions about fair access to its advantages. Ethical issues, such as algorithmic bias and the potential abuse of surveillance technologies, further highlight the necessity for responsible and inclusive AI development (El Aynaoui, Magri & Saran, 2023). Al Ghunaimi & Awashreh (2024) emphasized that the lack of explicit ethical standards and regulatory supervision in certain gulf states can intensify governance risks associated with AI-driven decision-making and data exploitation.

By investigating this issue, the research offers essential insights for policymakers and urban planners to facilitate sustainable and equitable urban growth. Additionally, addressing these questions contributes to ongoing discussions in digital infrastructure studies about how emerging technologies relate to socio-technical power dynamics, particularly in postcolonial urban settings (Graham, 2020; Awashreh, 2026). Although existing literature has thoroughly documented AI applications in smart cities, it often lacks sufficient focus on the unique socio-economic, cultural, and infrastructural circumstances of developing regions. This study aims to fill that gap by addressing these distinct challenges and suggesting strategies for effective AI integration. Al Ghunaimi and Awashreh (2025) contend that creating hybrid governance frameworks that integrate local cultural values with a flexible responsiveness to global digital influences is essential in preventing reliance on external sources while promoting innovation.

Additionally, the research identifies significant issues such as data protection, fairness, and ethical considerations, ensuring that AI-based solutions foster equitable urban development and resilience. In summary, this study underscores the potential of AI to assist the Global South in addressing the challenges of urbanization by improving the efficiency, sustainability, and responsiveness of urban systems (Awashreh, 2026). By tackling current obstacles and promoting adaptability, AI could revolutionize urban infrastructure and governance. Successful governance frameworks necessitate ongoing focus on involvement, motivation, and organizational efficiency, especially in rapidly evolving sectors influenced by technology (AlGhunaimi & AlGhenaimi, 2024). This change might lead to resilient cities that prioritize citizen needs, in line with global trends in urbanization and sustainability objectives.

The primary research question that directs this study is: How can Artificial Intelligence (AI) be effectively integrated into urban planning to tackle challenges like infrastructure constraints, skill shortages, and ethical issues in the Global South? In answering this question, the research highlights the significance of utilizing AI to enhance urban systems, promote inclusivity, and progress sustainability, ultimately aiding in the creation of fair and resilient smart cities.

The structure of this paper is organized as follows: Section 2 provides a literature review concerning urbanization and smart cities within developing countries. Section 3 details the methodology, which relies on secondary data. Section 4 shares findings from pertinent examples as well as results from primary data interviews, followed by a discussion of the pros and cons. The concluding section offers recommendations, societal implications, and practical considerations.

The research utilises two emerging critical frameworks to contextualize the role of AI in smart city development within the Global South: data colonialism and algorithmic governance. Couldry and Mejias (2019) define data colonialism as the extraction of human life through data appropriation, drawing a parallel to historical colonial extraction of resources. In several developing countries, the infrastructures for smart cities depend on AI systems that are developed and managed by foreign corporations, which can concentrate control over urban data flows, thereby undermining local ownership and agency (Taylor & Broeders, 2015).

Concurrently, algorithmic governance (Yeung, 2017) explains how AI systems increasingly influence decisions made in public administration, often utilizing opaque algorithms that lack democratic oversight. In the same vein, entrepreneurs operating within AI-driven business environments in the Global South must maintain a heightened sense of awareness to spot opportunity gaps in unpredictable and unstable situations, highlighting the necessity for flexible decision-making skills during crises (Al Ghunaimi et al., 2024; Sallem et al., 2024). This situation raises concerns about perpetuating social inequalities, algorithmic biases, and surveillance, particularly when implemented without sufficient ethical guidelines or community involvement (Ho, 2017; Kitchin, 2014). Similar concerns are raised by recent AI governance frameworks which highlight the lack of systematic regulation addressing algorithm transparency, privacy risks, and discrimination in service delivery in smart cities (Zhou & Kankanhalli, 2021). Employing these frameworks enables a critical examination of the socio-political ramifications of AI-based urban planning, redirecting the focus from mere technical efficiency to considerations of power, justice, and sovereignty in the digital era. The conceptual framework of the study, depicted in Fig. 1, combines these critical theories to highlight the dynamic interactions among AI technologies, governance structures, and socio-political outcomes in the development of smart cities.

Conceptual Framework

Drawing from the theoretical constructs of data colonialism and algorithmic governance, this study presents a conceptual framework that illustrates the integration dynamics of AI in smart cities within the Global South (refer to Fig. 1). The framework highlights three principal dimensions: (i) the primary drivers that motivate the adoption of AI in urban planning, (ii) the fundamental AI applications that facilitate smart city operations, and (iii) the emerging challenges that may intensify existing inequalities if not addressed through ethical and inclusive governance frameworks.

This research employs a descriptive qualitative methodology to examine the subjects and offer relevant examples when possible. This approach was selected for its capacity to deliver profound insights into the phenomena being studied, free from the limitations of quantitative data. All participants were adults and professionals, and their involvement was entirely voluntary. No ethical standards were violated during the research. An internal ethical review was carried out at A’Sharqiyah University, and informed consent was secured from each participant prior to the interviews. A qualitative approach facilitates the exploration of participants' perceptions, experiences, and insights, which are vital for grasping the complexities of urban planning and AI integration in the Global South.

Participant Selection

The individuals chosen for the primary data were picked through purposive sampling from the researcher's professional connections. This method provided access to individuals with substantial expertise in urban planning and AI. To address this limitation, efforts were made to include a variety of perspectives by bringing in experts from both the academic and industry domains in Oman and the UAE. The 12 participants were selected based on their specializations and experience in urban planning, AI, and management perspectives.

Primary data were collected through semi-structured interviews with the chosen participants. Each interview involved a series of 23 open-ended questions aimed at examining the participants' professional experiences, insights, and the difficulties they encounter in the realm of urban planning and the integration of AI. The interviews were recorded, transcribed, and examined using thematic analysis.

For the analysis, the transcripts were initially coded inductively to pinpoint recurring themes and patterns. Thematic analysis was applied to organize these patterns into broader categories that represent significant issues and trends. To ensure reliability and validity, two researchers independently conducted the coding, and the themes were validated through a consensus-building process. Table 2 displays the themes that were derived from both the primary and secondary data, offering a basis for the analysis and interpretation of the findings.

The interviews revealed a shared view among specialists that Artificial Intelligence (AI) has the potential to revolutionize urban planning in the Global South. AI's capability to optimize resources, enhance service delivery, and promote sustainability addresses key issues such as swift urban growth and inadequate infrastructure. By utilizing real-time data concerning traffic, energy, and public health, AI facilitates adaptive, data-informed decision-making, thereby improving urban flexibility and resilience. Unlike conventional static planning methods, AI’s real-time functionality and integration with smart infrastructure render it crucial for anticipating future demands and tackling contemporary urban problems, positioning it as vital for creating resilient cities.

Additionally, AI is already reshaping urban systems, with several instances illustrating its effects on traffic regulation, energy efficiency, and public safety. These practical examples underscore AI's advantages while also bringing to light ongoing infrastructural and governance challenges encountered by many regions in the Global South (refer to Table 3).

For example, in Lagos, Nigeria, systems utilizing AI help alleviate congestion by processing data from cameras, GPS, and social media to optimize traffic signal timings, leading to reduced commute times and lower emissions. Likewise, Singapore's adaptive traffic signals improve efficiency by leveraging real-time data (Interview urban planning academician, 2024). These achievements emphasize the ability of AI to scale solutions for congestion on a global scale.

In the energy domain, smart grids in cities like Amsterdam and New York forecast demand, enhance distribution efficiency, and minimize waste. Similarly, innovations in water management in Accra, Ghana, utilize AI to anticipate demand and boost conservation efforts, significantly decreasing water loss.

AI is also advancing public safety. For instance, in the UK, AI systems oversee public areas, identify unusual activities, and forecast areas likely to experience crime, facilitating proactive law enforcement. In Medellín, Colombia, AI aids urban planning by evaluating socioeconomic and infrastructure data to model growth scenarios (Interview urban planning expert, 2024). However, these technologies raise concerns about privacy, highlighting the importance of strong encryption measures to balance AI's advantages with the safeguarding of personal rights.

Despite its promise, AI confronts various challenges, especially in developing areas. Insufficient digital infrastructure is a significant barrier, as seen in Accra, where outdated systems hindered the rollout of AI-based water management (Interview AI academician, 2024). Similarly, in Bangalore, AI initiatives aimed at resolving water scarcity faced limitations from infrastructural deficiencies and the imperative for improved interagency cooperation. Moreover, issues related to data availability and quality restrict AI’s potential. In Lagos, for instance, the deployment of AI-driven traffic systems depended on precise data, yet securing reliability and fostering public confidence continue to pose major hurdles. Additionally, the lack of skilled professionals often leads to reliance on external advisors, as observed in Medellín's AI urban planning initiatives (Interview academician, 2024).

To overcome these obstacles, it is crucial to invest in infrastructure, promote public-private collaborations, and support capacity-building strategies. For example, the integration of IoT devices in Accra has resulted in better water management, despite existing infrastructure challenges. Moreover, training programs focused on developing local expertise can decrease dependence on outside consultants (Interview urban expert, 2024). Engaging the community is also vital to ensure AI solutions resonate with local demands. In Lagos, public involvement was fundamental to the success of AI-enhanced traffic systems. These approaches can foster trust, inclusion, and the sustainable adoption of AI in urban frameworks (Interview AI expert, 2024). As Morozov (2013) notes, without effective institutional safeguards, smart city technologies may become instruments of surveillance capitalism rather than facilitators of democratic governance. Nevertheless, such participatory frameworks are uncommon in the Global South, where regulatory capture and weak institutional capacities frequently restrict public discourse regarding algorithmic governance (Yeung, 2017; Ho, 2017).

Ethical and sustainability issues are crucial to the effective implementation of AI. Transparent governance is necessary to tackle challenges such as algorithmic bias, privacy infringements, and data misuse. Ethical frameworks emphasize the need for explainability, fairness audits, and participatory oversight to build trust in AI-enabled urban systems (Popescul & Radu, 2025). Helsinki serves as a compelling example, having created public dashboards to clarify AI decision-making processes, which helps in building trust and accountability (Interview by AI academician, 2024). Regarding sustainability, the recognition of AI’s contribution to green infrastructure and climate resilience is widespread, although its energy-intensive characteristics pose difficulties. Cities like Bangalore and Accra have incorporated AI with IoT to save resources and enhance efficiency, aligning their urban systems with environmental objectives. Achieving a balance in these trade-offs will necessitate the use of renewable energy and the creation of international guidelines to ensure responsible AI utilization (Interview by urban planning expert, 2024). Coordinated efforts among governments, international organizations, educational institutions, and the private sector are essential to navigate AI implementation challenges in urban planning. Successful examples from cities like Medellín, Lagos, Bangalore, and Accra illustrate the powerful impact of AI when it is paired with strategic investments, efforts to build capacity, and active community participation (Interview urban planning expert, 2024). A crucial approach to tackling infrastructure issues involves governments and organizations investing in strong digital foundations. Essential elements such as high-speed internet, data centres, and cloud-based platforms are critical for facilitating AI applications in areas like traffic management and energy systems. For instance, in Accra, AI-assisted water management solutions have effectively decreased water losses in spite of existing infrastructure issues. Public-private partnerships (PPPs) can significantly help in closing the digital divide, as shown by India's Smart Cities Mission, where a mix of public investment and private sector innovation improved urban technological structures (Interview academician, 2024).

In addition to the need for solid digital infrastructure, closing the skills gap is a vital component for fostering effective urban development. Capacity-building programs and educational reforms are essential to bridge this skills gap. Educational institutions should provide AI and urban planning courses to prepare future professionals with the necessary capabilities for urban development ahead. In Medellín, AI-supported urban planning underlines the significance of nurturing local expertise to sustain and expand projects. Moreover, collaboration among governments, academic institutions, and private entities on professional training initiatives, coding boot camps, and internships can empower local talent. This would reduce dependence on external consultants and encourage sustainable urban growth (Interview urban planning academician, 2024).

In addition to infrastructure and skill enhancement, promoting active community engagement is equally critical for ensuring the effectiveness and inclusivity of AI technologies. Involving citizens cultivates trust, aligns AI with community values, and enhances social acceptance. For example, workshops, consultations, and digital platforms provide avenues for residents to express their concerns, propose enhancements, and impact AI design. In Lagos, citizen participation played a crucial role in the successful implementation of an AI-based traffic management system, which significantly lowered commute times and emissions (Interview AI academician, 2024). Furthermore, public-private partnerships are essential for scaling AI initiatives in urban planning. These collaborations enable governments to access private sector innovation and funding while ensuring transparency and public benefit. The EU’s Urban Innovative Actions program serves as a prime illustration of how cross-sector partnerships can produce significant, citizen-centric AI solutions (Interview academician, 2024).

Addressing ethical considerations is vital in the deployment of AI, particularly in protecting citizens' rights, tackling inequalities, and fostering social welfare. Proactive measures are necessary to address concerns such as algorithmic bias, privacy breaches, and misuse of surveillance. AI systems based on non-representative data can perpetuate inequalities, adversely affecting marginalized groups. Experts advise employing diverse datasets, fairness metrics, and performing regular audits to diminish such risks. New York City’s Automated Decision Systems Task Force is a case in point of these proactive strategies, ensuring equity in the application of AI (Interview urban planning expert, 2024). Moreover, AI-driven surveillance technologies, such as facial recognition, bring about privacy issues, including unauthorized data collection. To address these challenges, clear regulations and ethical guidelines are required. Transparent governance practices, such as Helsinki’s public dashboards, foster trust and accountability in AI decision-making (Interview academician, 2024). Consequently, cities must establish strong frameworks that emphasize transparency, accountability, and fairness. Continuous audits and impact evaluations can assess the societal effects of AI, ensuring it aligns with ethical norms and protects citizens' rights (Interview AI academician, 2024).

In light of the significance of transparency and ethical standards in AI governance, applications of AI in sustainable urban development further demonstrate its transformational capacity to tackle environmental and infrastructure issues. AI is increasingly acknowledged as a crucial factor in promoting sustainable urban growth by optimizing resource usage, minimizing energy consumption, and facilitating green infrastructure. In cities like Amsterdam and San Diego, artificial intelligence systems forecast energy demand and oversee smart grids, reducing expenses and lessening environmental effects. Moreover, AI assists urban planners in creating energy-efficient structures and preserving green spaces. In Singapore, predictive modelling integrates greenery into densely populated areas, enhancing air quality and alleviating the urban heat island phenomenon (Interview AI academician, 2024).

Furthering its significance in urban development, AI's application reaches essential fields such as climate change adaptation and disaster response, showcasing its ability to tackle global environmental issues. Predictive analytics enable cities to foresee hazards like floods or heatwaves, allowing for preventive actions. For instance, in Japan, AI tracks seismic activities and improves emergency responses during earthquakes. AI-driven water management frameworks streamline distribution during adverse weather, guaranteeing sustainable resource utilization amidst climatic challenges (Interview AI expert, 2024).

Nonetheless, AI's promise comes with compromises. The substantial energy demand involved in training machine learning models and running data centres may contradict sustainability ambitions. Additionally, creating and disposing of advanced technology hardware contributes to environmental implications. To mitigate these challenges, embracing circular economy initiatives and transitioning to renewable energy sources for AI functions is essential (Interview AI expert, 2024).

While AI offers meaningful solutions to environmental issues, its integration with robust policy frameworks is just as critical for ensuring that its application aligns with overarching sustainability goals and ethical considerations. Besides tackling urgent climate issues, the involvement of local governments and international policies is paramount for ensuring that AI technologies correspond with sustainability aims. Experts emphasize the importance of regulatory frameworks that encourage energy-efficient AI solutions and set standards for evaluating AI's ecological effects. For example, the European Union's Green Digital Agenda consolidates policies to guarantee that AI systems adhere to ethical and sustainable benchmarks, providing a template for worldwide best practices (Interview AI expert, 2024).

Additionally, promoting collaborations among governments, research organizations, and private sector entities enhances the innovation and execution of AI-based solutions, bridging the divide between policy and practice in sustainable development. Such partnerships facilitate the sharing of resources, expertise, and knowledge to address intricate urban issues. The EU’s Urban Innovative Actions program exemplifies this intersectoral collaboration, fostering AI-driven, sustainable, and inclusive approaches (Interview urban planning expert, 2024). Furthermore, effective governance structures must incorporate mechanisms for transparency and accountability to ensure that AI implementations align with societal ideals and environmental goals. As one expert pointed out, “Incorporating ethical principles into policymaking enables cities to steer AI development towards long-term advantages while tackling immediate issues such as energy usage and equity” (Interview urban planning expert, 2024).

Looking to the future, the prospects for AI in urban planning are expanding, promising innovative solutions for sustainability and resilience. AI possesses the transformative capacity to impact urban development, optimizing resource allocation, strengthening disaster readiness, and bolstering green infrastructure. By embedding AI within urban planning, cities can transform into sustainable and inclusive ecosystems. Examples like Singapore and Amsterdam illustrate how AI advancements can boost energy efficiency and climate adaptability, presenting scalable approaches for other locales. However, the future trajectory of AI in urban planning hinges on its alignment with sustainability ideals and solid governance. Confronting challenges such as excessive energy consumption and dependence on resources will necessitate strategies like adopting renewable energy for AI functionalities and developing circular economy practices for e-waste management (Interview AI academician, 2024). By merging AI’s technological potential with ethical perspectives and comprehensive policies, cities can realize its full capabilities, establishing resilient, equitable, and sustainable urban environments that cater to present and future communities.

There is a shared understanding among interviewees that AI technologies are being increasingly utilized in smart cities within the Global South, particularly in sectors such as traffic management, waste collection optimization, and analysis of public health data. In cities like Jakarta, Indonesia, AI-based traffic management solutions assist in alleviating congestion by modifying traffic signals according to real-time data, enhancing traffic flow, and lowering emissions. AI-driven waste management solutions improve collection routes and anticipate bin capacity, improving service delivery while reducing operational costs. Moreover, AI is transforming public health by forecasting disease outbreaks, monitoring infections, and optimizing resource distribution, hence enhancing healthcare in areas with limited infrastructure. However, the integration of AI faces numerous hurdles. Insufficient infrastructure—like poor roads, unreliable electricity, and inconsistent internet access—complicates the deployment of AI systems that depend on advanced infrastructure. The significant initial costs of AI technologies pose another obstacle, particularly for nations dealing with immediate challenges such as poverty reduction and basic infrastructure development. In addition, rapid urbanization exerts pressure on urban planning systems and public services, often outstripping the ability to implement modern technologies effectively.

Notwithstanding these challenges, the potential advantages of AI in urban planning are considerable. AI can enhance efficiency by streamlining traffic management and waste collection, leading to cost reductions and improved resource utilization. It also promotes sustainability by optimizing energy grids and incorporating renewable sources, aiding in the development of greener cities. AI has the potential to drive economic growth by cultivating new sectors like smart agriculture and renewable energy, thereby generating employment and invigorating local economies. In healthcare and education, AI can boost service delivery, making healthcare more accessible and offering remote learning opportunities to underserved populations.

Nonetheless, experts caution that the adoption of AI could aggravate inequality if marginalized groups are excluded from these technological advancements, widening social and economic divides. Instead, AI operates as a socio-technical assemblage influenced by unequal global economic interests, policy frameworks, and corporate governance standards that frequently prioritize efficiency and profitability over fairness and autonomy (Zuboff, 2019; Ho, 2017). Data privacy also raises concerns, particularly in nations with feeble data protection regulations, as the misuse of personal information could erode trust in AI solutions. An over-dependence on technology in countries with limited technical knowledge might create vulnerability, leaving these nations exposed to cyberattacks or failures in externally managed systems. In summary, while AI holds tremendous promise for enhancing urban planning and quality of life in the Global South, its effective implementation necessitates careful stewardship. Addressing infrastructure shortcomings, resolving resource limitations, and confronting social inequalities will be crucial for realizing AI's full capacity in developing efficient, sustainable, and inclusive smart cities. This research offers fresh perspectives on how AI systems mirror and perpetuate global power dynamics in urban governance, providing a critical viewpoint from the Global South that is frequently overlooked in the field of human-computer interaction.

The outcomes of this research emphasize the necessity of critically examining the political frameworks that underpin the implementation of AI in urban environments of developing regions. Instead of perceiving AI solely as a neutral technological instrument, its application mirrors transnational power relations that influence who have control over urban data, which interests are prioritized, and whose perspectives are sidelined. As Couldry and Mejias (2019) assert, data colonialism poses the risk of treating urban populations as mere sources for extractive data flows that primarily benefit external corporate entities, while algorithmic governance (Yeung, 2017) introduces new instances of opaque, undemocratic decision-making processes that may evade public oversight.

The results emphasize the significance of interpreting AI implementation not merely as a technological advancement, but as a political and economic phenomenon influenced by global power structures (Couldry & Mejias, 2019). AI-enabled smart cities in the Global South are at risk of perpetuating colonial-style dependency, as data infrastructures frequently fall under the control of foreign technology firms, thus undermining national data sovereignty and the capacity for self-determination (Taylor & Broeders, 2015). In the absence of locally adapted governance models, AI systems designed for Global North contexts may exacerbate inequality and fail to address Global South-specific socio-political realities (Wall, Saxena, & Brown, 2021). This calls for not only enhancing technical capabilities but also for democratizing data governance frameworks that prioritize marginalized populations and actively oppose extractive data practices (Couldry & Mejias, 2019; Kitchin, 2014). This aligns with broader critiques that suggest digital infrastructures increasingly embody "platform imperialism," where Western corporations dominate the global digital landscape and diminish local digital sovereignty (Jin, 2015).

Nonetheless, despite its promise, numerous challenges impede the adoption of AI in developing areas, as highlighted in the findings. Insufficient digital infrastructure and a lack of skilled workforce were prevalent obstacles, as confirmed by both the primary data collected from expert interviews and secondary literature. This lack of preparedness intensifies the digital divide, hindering the fair distribution of the advantages of AI (Ansari et al., 2022). Moreover, ethical issues such as algorithmic biases and concerns regarding data privacy emerged as significant barriers to public confidence, a point that was consistently highlighted in responses from experts. These challenges underscore the need for solid regulatory frameworks and transparent governance strategies to guarantee accountability and equitable implementation (Kitchin, 2016). To systematically tackle these ethical challenges, Table 4 summarizes essential governance strategies that align with responsible AI application in smart cities.

Figure 2 summarizes the key barriers and enablers identified in this study for responsible AI deployment in urban planning within Global South.

A crucial finding from the analysis highlights the significance of developing strong metrics to assess the effectiveness of AI, such as accuracy of predictions, response times, and societal impacts like enhancements in quality of life (Hammoumi, Maanan & Rhinane, 2024). Additionally, factors like scalability and public trust were recognized as essential for successful AI implementation, which requires transparent methods and initiatives aimed at raising community awareness. These insights bolster the methodological focus on participatory approaches and collaboration among stakeholders to improve inclusivity and responsiveness. In the end, addressing these challenges will necessitate that policymakers invest in digital infrastructure and workforce development while emphasizing ethical protections. Joint efforts among governments, businesses, and local communities will be vital to ensure that AI projects are in line with broader development objectives and aid in the formation of effective, sustainable, and inclusive smart cities.

Figure 3 displays a consolidated model that encapsulates the complex interactions between AI applications, governance structures, and the challenges highlighted throughout the findings.

The incorporation of Artificial Intelligence (AI) into urban planning offers a groundbreaking opportunity to address the challenges associated with swift urban growth, particularly in the Global South. The results of this study, based on both primary and secondary sources, highlight AI’s potential to enhance resource management, improve decision-making processes, and elevate urban living standards. Case studies from cities like Accra, Bangalore, Lagos, and Medellín demonstrate how AI has been successfully applied in areas such as waste management, water conservation, traffic flow, and urban development. These instances validate the study’s approach and showcase AI’s ability to cut down on inefficiencies, preserve resources, and promote sustainable development. Nonetheless, these cases also reveal the intricate relationship among technology, local infrastructure capabilities, regulatory preparedness, and governance practices that significantly shape AI's effectiveness in urban environments. Furthermore, the adoption of AI in urban governance cannot be separated from broader issues concerning global power disparities, accountability in governance, and ethical dilemmas. In the Global South, where regulatory oversight may be lacking, the use of AI poses risks of exacerbating the digital divide, perpetuating surveillance systems, and deepening reliance on foreign-controlled systems. Therefore, the development of AI-infused smart cities requires not only continuous technological advancements but also flexible ethical governance frameworks that enhance local data sovereignty, maintain transparency, and incorporate participatory policymaking at all levels of governance. Beyond merely technological answers, the responsible implementation of AI in the Global South necessitates ongoing scrutiny of governance frameworks, power imbalances, and data ownership issues. In the absence of regulatory protections, AI could worsen digital disparities, facilitate unchecked surveillance, and reinforce social exclusion. Additionally, the commercialization of urban data contributes to what some scholars refer to as 'platform urbanism,' where urban governance increasingly relies on private digital platforms that operate without public oversight (Barns, 2019).

Subsequent research should examine who stands to gain from AI-led urban development, whose data is being harvested, and how power dynamics shift within digitally mediated governance structures. Besides that, long-term studies assessing the social and environmental repercussions of AI implementation over time are essential to guide evidence-based policy adjustments. Ethical considerations, such as biases in algorithms and risks to privacy, add complexity to the deployment of AI solutions, highlighting the necessity for comprehensive regulatory frameworks and governance measures. This study emphasizes the vital importance of engaging communities to ensure AI technologies align with local demands. By employing participatory approaches and transparent practices, stakeholders can foster trust and guarantee the acceptance of AI-powered solutions. It is essential to prioritize ethical safeguards and sustainability strategies throughout the implementation process, ensuring fairness, inclusivity, and environmental responsibility.

Looking forward, AI holds tremendous promise for tackling urbanization issues. By harnessing its abilities in resource optimization, the development of green infrastructure, and building climate resilience, AI can help establish sustainable, just, and robust urban systems. True progress in overcoming existing challenges relies on collaboration among stakeholders and targeted investments to realize AI’s full potential, ensuring a positive influence on urban communities globally. Ultimately, guaranteeing a comprehensive, fair, and accountable integration of AI into urban governance is a collective obligation of policymakers, technology creators, civil society, and international bodies.