Artificial Intelligence-Driven Predictive Analytics for Sustainable Urban Planning

Introduction: Challenges for sustainable urban development are especially related to resource demand, levels of pollution, and infrastructure planning for urbanization. Faced with rapid urban growth we must adopt new approaches to ensure most of the human race continues to live in cities yet these remain liveable and resilient. Predictive analytics using AI has the potential to improve urban planning by using it to manage resources and increase environmental sustainability.Methodology: To study the effect of AI on urban growth and sustainability, a randomized block design was applied. Machine learning (ML) models trained with data from municipal sources, remote sensing platforms, and socioeconomic surveys using neural networks, decision trees, and support vector machines are constructed. Predicted values of the key urban parameters were obtained and the accuracy of the model was measured by MAE, RMSE, and cross-validation.Results: The models of AI showed that Metropolis City would see a 15 percent increase in energy demand and a 12 percent increase in water consumption. AI predicts also areas where to increase green spaces and make more efficient public transport. AI solutions proposed ways towards better environmental and resource management despite higher pollution levels and waste generation.Conclusion: Predictive models designed by AI offer a great deal of information on how cities can go green and manage growth as well as resources use. Inspired, particularly through neural networks, these models show high potential for infrastructure optimization in the city, environmental sustainability improvement, and realizing resource management.