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<p>As urban populations continued to rise, cities faced increasing challenges in land use management, infrastructure provision, environmental protection, and social equity. Addressing these complexities required approaches that were both data-driven and responsive to decision-making needs. This review examined the integration of Geographic Information Systems (GIS), Artificial Intelligence and Machine Learning (AI/ML), and Multi-Criteria Decision Analysis (MCDA) as a comprehensive framework for sustainable urban settlement planning. Drawing from 100 peer-reviewed studies published between 2017 and 2025, the paper evaluated the theoretical foundations, individual capabilities, and combined potential of these tools. GIS provided the spatial foundation through mapping and data layering, AI/ML contributed predictive and adaptive modelling, and MCDA incorporated structured prioritization for complex decision-making. These components interacted in a sequential but iterative manner, where GIS outputs informed AI/ML models, and both collectively served as inputs for MCDA. The integration was flexible and could be adapted depending on the planning context. Together, these technologies enhanced the capacity for land suitability analysis, urban growth forecasting, informal settlement detection, and resilience planning. Case studies from diverse regions demonstrated the effectiveness of this integrated approach in enhancing planning accuracy and stakeholder engagement. The review also identified operational, technical, and governance-related challenges, including data fragmentation, interoperability limitations, and ethical concerns. Finally, it highlighted future directions that emphasized participatory planning, open data standards, and adaptive modelling. The integrated GIS–AI/ML–MCDA framework was found to provide a scalable and inclusive pathway to guide cities toward sustainability and resilience in the context of rapid urbanization. The review’s distinct contribution lay in demonstrating how the integration of GIS, AI/ML, and MCDA offered a unified, adaptive framework that strengthened the links between sustainability and resilience in urban settlement planning.</p>

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Integrating Geographic Information System, Artificial Intelligence, and Multi-Criteria Decision Analysis: A Comprehensive Review for Sustainable Urban Settlement Planning

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • S. K. Ray

摘要

As urban populations continued to rise, cities faced increasing challenges in land use management, infrastructure provision, environmental protection, and social equity. Addressing these complexities required approaches that were both data-driven and responsive to decision-making needs. This review examined the integration of Geographic Information Systems (GIS), Artificial Intelligence and Machine Learning (AI/ML), and Multi-Criteria Decision Analysis (MCDA) as a comprehensive framework for sustainable urban settlement planning. Drawing from 100 peer-reviewed studies published between 2017 and 2025, the paper evaluated the theoretical foundations, individual capabilities, and combined potential of these tools. GIS provided the spatial foundation through mapping and data layering, AI/ML contributed predictive and adaptive modelling, and MCDA incorporated structured prioritization for complex decision-making. These components interacted in a sequential but iterative manner, where GIS outputs informed AI/ML models, and both collectively served as inputs for MCDA. The integration was flexible and could be adapted depending on the planning context. Together, these technologies enhanced the capacity for land suitability analysis, urban growth forecasting, informal settlement detection, and resilience planning. Case studies from diverse regions demonstrated the effectiveness of this integrated approach in enhancing planning accuracy and stakeholder engagement. The review also identified operational, technical, and governance-related challenges, including data fragmentation, interoperability limitations, and ethical concerns. Finally, it highlighted future directions that emphasized participatory planning, open data standards, and adaptive modelling. The integrated GIS–AI/ML–MCDA framework was found to provide a scalable and inclusive pathway to guide cities toward sustainability and resilience in the context of rapid urbanization. The review’s distinct contribution lay in demonstrating how the integration of GIS, AI/ML, and MCDA offered a unified, adaptive framework that strengthened the links between sustainability and resilience in urban settlement planning.