Using ScienceDirect, Scopus and Sabinet databases, this study found that the smart cities in emerging economies comprised 162 published research publications from all time periods without any constraints. The following setup was utilised to source pertinent articles from 2017 to 2024 aside from the current year. One particular query is: (Title-key) smart cities, (Title-key) emerging economies and (Title-key) AI and IoT. Other characteristics, like the year of publication (2017–2019), the document type (article 97), conference articles (45) and journals with book series (20), were also employed to increase the efficacy of the research cycle. 40 documents were the focus of this study and were utilised for further analysis after the parameters were put into place. In addition, the government and corporations were included in the various categories. A further stage in completing the characteristics of the selected articles for this study is locating pertinent journals and their individual contributions to Policy Frameworks for Emerging Economies Sustainable Smart City development.

ScienceDirect, Scopus and Sabinet databases were used in this study to find 162 articles from 2017 to 2024 that included all of the terms, emerging economies, developing countries, governance, AI, IoT and Smart cities. To make the study more realistic and practical, 40 documents were selected based on a single criterion. The document must contribute to the Policy Frameworks for Emerging Economies Sustainable Smart City Development. The adoption of AI and IoT for the revolutionisation of urban infrastructure development. The articles offer a comprehensive grasp and justification of how AI and the integration of IoT and AI technology are transforming the development of urban infrastructure and providing game-changing answers to persistent problems in public services, energy, transportation and governance (see Figure 2).

Ethical clearance to conduct this study was obtained from the University of South Africa College of Science, Engineering and Technology School of Computing Ethics Research Committee (No. 6109).

In growing economies, the combination of IoT and AI technology is transforming the development of urban infrastructure and providing game-changing answers to persistent problems in public services, energy, transportation and governance (Nieto Bernal & García Espitaleta, 2021). While AI’s predictive analytics and machine learning capabilities enable cities to optimise resource allocation, prevent infrastructure failures and improve decision-making processes, the IoT network of interconnected sensors allows for real-time data collection on everything from air quality to traffic flows (Bibri & Krogstie, 2020). In emerging nations, where increased urbanisation puts a strain on current systems and necessitates innovative solutions that circumvent conventional growth pathways, these technologies have a particularly significant impact. But for them to be implemented successfully, policy frameworks that maximise public benefits while addressing social, economic and technological hurdles are necessary (Allam & Dhunny, 2019).

This study has noted that through dynamics and modification of signal timings and public transportation routing, IoT-enabled smart traffic systems and AI-driven predictive modelling are easing traffic in places like Jakarta and Bangalore (Alsamhi et al., 2021). Similarly, data-optimised minibus routes enabled by AI-powered demand-responsive transport are enhancing last-mile connectivity in African cities. Another crucial application is energy management, where cities like Santiago and Mumbai can integrate renewable energy sources and minimise distribution losses by using smart grids with IoT sensors and AI-based load predictions (Ghasempour, 2019). These technological applications show how data-driven infrastructure can improve sustainability, but they need rules that guarantee interoperability across systems and standardise IoT deployment standards (Bhattacharya et al., 2020).

Through innovative applications, AI and IoT are revolutionising urban administration and service delivery beyond physical infrastructure. While IoT-based water quality monitoring is averting disease outbreaks in informal settlements, AI-enabled chatbots and voice assistants are improving access to municipal services in multilingual communities (Anthopoulos, 2017). Although these applications create significant ethical policy problems regarding monitoring and bias, predictive policing technologies that use AI pattern recognition are assisting understaffed police forces in efficiently allocating resources (Sadowski & Spachos, 2020). Most notably, cities are now able to test infrastructure scenarios and climate adaptation plans prior to execution because of digital twin technology, which combines IoT sensor networks with AI simulation models (Kitchin, 2021). In order to guarantee that technologies serve the public interest rather than worsen inequality, these applications emphasise the necessity of regulations that strike a balance between innovation and responsibility.

Akande et al. (2020) emphasise that future-focused policy frameworks that fully utilise AI and IoT while tackling systemic issues are necessary for the development of smart cities in emerging countries. Adaptive governance approaches that strike a balance between equitable development and technological innovation must be given top priority in future directions. Aghimien et al. (2022) further highlight that to enable iterative testing of AI and IoT solutions while preserving strong protections for data privacy and algorithmic accountability, policymakers should concentrate on developing dynamic regulatory sandboxes. These frameworks must be both designed to guard against exploitation and guarantee the general welfare while remaining adaptable enough to handle the quick advancements in technology. The creation of mechanisms for cross-border digital cooperation that allow emerging economies to exchange best practices while preserving data sovereignty is a crucial area for development (Jonek-Kowalska & Wolniak, 2022).

Antwi-Afari et al. (2021) adds the creation of sustainable funding structures is yet another important avenue for future policy. For that reason, emerging economies must create novel financial instruments suited to smart city projects to transcend conventional public funding patterns. This entails investigating impact investment funds that concentrate on urban technology solutions, blockchain-enabled municipal bonds and performance-based contracting models that link private sector compensation to quantifiable gains in urban sustainability measures. Alderete (2022) adds that future regulations should also encourage the growth of regional AI and IoT ecosystems by reducing reliance on foreign suppliers through technology transfer initiatives, startup incubators and focused research and development (R&D) investments. These strategies can create high-value jobs in the digital economy and encourage domestic innovation.

A study by Li et al. (2019) emphasise that equal policy emphasis must be given to the human aspect of smart city development. Participatory design techniques that involve individuals in co-creating urban tech solutions, especially for underprivileged communities, must be institutionalised in future frameworks. Thus, to guarantee accessibility for all urban dwellers, irrespective of their degree of digital literacy, policies should require AI interfaces and IoT devices to adhere to universal design standards. Janani et al. (2021) assert that programs for workforce transition must be created concurrently to address automation-related job displacement, with a focus on reskilling employees in industries most at risk from AI disruption. To build genuinely inclusive smart cities that ensure no one is left behind in the digital transformation, these social strategies must be combined with technology roadmaps.

Ashwini and Savithramma (2022) claim that future smart city policies must make climate resilience a top priority. To build climate-adaptive urban infrastructure, emerging economies should design integrated frameworks that merge IoT sensor networks with AI-powered predictive analytics. This includes laws that support smart solutions rooted in nature, such AI-enhanced green areas and IoT-powered urban agricultural systems. Future regulatory frameworks should set requirements for energy-efficient AI computation in urban applications and mandate climatic stress testing for all significant smart city projects. Emerging economies have the chance to advance to sustainable urban development models that concurrently solve technology and environmental concerns as a result of the convergence of the digital and green transitions (Bokhari & Myeong, 2022).

Furthermore, strong mechanisms for ongoing learning and adaptation must be incorporated into future policy frameworks. This entails setting up observatories to track the effects of smart cities, formulating uniform metrics to assess AI and IoT deployments and establishing knowledge-sharing platforms among Global South cities (Ismagilova et al., 2019). Therefore, emerging economies should create policy frameworks that use AI and IoT not only to advance technology but also to make cities more sustainable, habitable and equitable for all citizens by adopting these future orientations (Ahad et al., 2020).

The importance of scalable and reasonably priced solutions in guaranteeing long-term success is another key finding. Cities that put an emphasis on energy-efficient AI models and modular, inexpensive IoT devices have seen faster adoption and higher returns on investment. Before moving on to more sophisticated AI applications, several towns, for instance, employed basic sensor networks for water or trash management. However, the survey points out problems including data silos, a lack of interoperability and local stakeholders’ reluctance to change. Strong community involvement, capacity-building initiatives and transparent explanations of the advantages of smart technologies are frequently found in successful examples. These findings imply that, although technology is an essential facilitator, social and organisational elements are just as significant in the creation of sustainable smart cities.

The study emphasises the necessity of more vigorous regulatory frameworks to handle ethical, security and privacy issues. Public mistrust can impede the deployment of IoT and AI in areas with lax or inconsistent data protection regulations. On the other hand, citizens have been more accepting of cities that have put in place open data governance procedures and cybersecurity safeguards. The study also draws attention to the widening digital divide adoption, which puts metropolitan areas with little resources in danger of deteriorating further in the absence of focused initiatives. The report suggests financial structures, knowledge-sharing platforms and international cooperation to close this gap and promote the development of smart cities in an equitable manner.

Despite their revolutionary potential, emerging economies have a difficult time implementing AI and IoT technologies for the construction of smart cities. The introduction of IoT systems is hampered by several significant obstacles, including a lack of sensor networks, erratic power supplies and restricted Internet access (Anthopoulos, 2017). Effective machine learning is hampered by fragmented or nonexistent datasets in many cities, which lack the fundamental data ecosystems required to support AI applications (Kitchin, 2021). These problems are made worse by financial limitations, since municipal budgets in underdeveloped countries sometimes cannot support the long-term upkeep of smart technology or pay for their high upfront expenditures (Allam & Dhunny, 2019). The digital divide between smart cities in rich and emerging economies will keep growing in the absence of creative funding solutions such as PPPs or mixed finance mechanisms (Sanchez Guzman et al., 2020).

Good implementation is also severely hampered by institutional and governance issues. Smart city initiatives are hampered by bureaucratic inefficiencies and overlapping authorities in many emerging nations with fragmented municipal administration (Kummitha & Crutzen, 2019). Concerns regarding technology lock-in and loss of sovereignty over urban data are raised by local governments’ reliance on foreign vendors because of a lack of technical experience (Sadowski & Spachos, 2020). Conflicting policies are another common consequence of poor policy coordination between national and local authorities, especially when it comes to data privacy, cybersecurity and AI ethics (Ma & Zhu, 2022). For instance, insufficient legal frameworks run the risk of permitting biased policing or mass surveillance, particularly in politically unstable areas, even though IoT surveillance may improve public safety (Yigitcanlar et al., 2020).

Another significant issue is socioeconomic inequality, since smart city technologies frequently favour wealthy metropolitan regions while underserving low-income and informal settlements (Angelidou, 2017). In emerging economies, where a sizable portion of the population lacks access to smartphones or digital literacy, the digital gap is especially severe, preventing people from using AI-driven public services (Bibri & Krogstie, 2020). Additionally, unless inclusive reskilling strategies are put in place, the automation of municipal services through AI may result in the displacement of low-skilled workers, worsening unemployment (March & Ribera-Fumaz, 2021).