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The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities

Author

Listed:
  • Elda Cina

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Ersin Elbasi

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Gremina Elmazi

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Zakwan AlArnaout

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

Abstract

As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities.

Suggested Citation

  • Elda Cina & Ersin Elbasi & Gremina Elmazi & Zakwan AlArnaout, 2025. "The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities," Sustainability, MDPI, vol. 17(11), pages 1-39, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5148-:d:1671322
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    References listed on IDEAS

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    1. Ibrahim Mutambik, 2024. "Unlocking the Potential of Sustainable Smart Cities: Barriers and Strategies," Sustainability, MDPI, vol. 16(12), pages 1-16, June.
    2. Silvia Mazzetto, 2024. "A Review of Urban Digital Twins Integration, Challenges, and Future Directions in Smart City Development," Sustainability, MDPI, vol. 16(19), pages 1-33, September.
    3. Margarita Angelidou, 2017. "The Role of Smart City Characteristics in the Plans of Fifteen Cities," Journal of Urban Technology, Taylor & Francis Journals, vol. 24(4), pages 3-28, October.
    4. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    5. Volodymyr N. Skoropad & Stevica Deđanski & Vladan Pantović & Zoran Injac & Slađana Vujičić & Marina Jovanović-Milenković & Boris Jevtić & Violeta Lukić-Vujadinović & Dejan Vidojević & Ištvan Bodolo, 2025. "Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics: A Sustainable Approach to Improving Urban Mobility in the City of Belgrade," Sustainability, MDPI, vol. 17(8), pages 1-31, April.
    6. Man Zhang & Dongwei Tian & Jingming Liu & Xuehua Li, 2024. "Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing," Sustainability, MDPI, vol. 16(17), pages 1-21, August.
    7. Eirini Aivazidou & Georgios Banias & Maria Lampridi & Giorgos Vasileiadis & Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2021. "Smart Technologies for Sustainable Water Management: An Urban Analysis," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
    8. Berk Kaan Kuguoglu & Haiko van der Voort & Marijn Janssen, 2021. "The Giant Leap for Smart Cities: Scaling Up Smart City Artificial Intelligence of Things (AIoT) Initiatives," Sustainability, MDPI, vol. 13(21), pages 1-16, November.
    9. Baha M. Mohsen, 2024. "AI-Driven Optimization of Urban Logistics in Smart Cities: Integrating Autonomous Vehicles and IoT for Efficient Delivery Systems," Sustainability, MDPI, vol. 16(24), pages 1-24, December.
    10. Rob Roggema, 2017. "The Future of Sustainable Urbanism: Society-Based, Complexity-Led, and Landscape-Driven," Sustainability, MDPI, vol. 9(8), pages 1-20, August.
    11. Ferreira dos Santos, Joao Pedro & de Matos, Celso Augusto & Groznik, Aleš, 2025. "The role of artificial intelligence in smart city systems usage: drivers, barriers, and behavioural outcomes," Technology in Society, Elsevier, vol. 81(C).
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