AI-Driven Urban Expansion Simulation: Predicting Resource Use, Land Dynamics, and Sustainability in Riyadh

Urban expansion in rapidly developing regions, such as Riyadh, Saudi Arabia, presents significant challenges to resource allocation, land use planning, and socioeconomic equity. This study leverages artificial intelligence (AI) to simulate and predict the impacts of urban expansion policies on resource consumption, land use dynamics, and socio-economic sustainability. Using publicly available data from the “Data Saudi” platform, including economic indicators, land use zoning, and population density, this research integrates geospatial analysis and machine learning models to examine the interplay between urban growth and sustainability metrics. The study begins with an exploration of historical urban expansion trends and their resource implications, followed by predictive modeling of future scenarios based on proposed policy interventions. Key findings highlight potential trade-offs between resource efficiency and socio-economic outcomes, providing insights into sustainable urban development practices. By combining AI-driven analysis with policy scenario testing, this research offers a novel approach to data-driven urban planning in Saudi Arabia. Policymakers can utilize these findings to refine zoning regulations, enhance infrastructure development, and promote sustainable urban ecosystems. The methodology and results are transferable to other rapidly urbanizing regions worldwide, contributing to global sustainability goals.