The rapid development of artificial intelligence (AI) has revolutionized monitoring, forecasting, and managing complex environmental systems. This chapter delves into the role of AI in developing sustainable environmental prediction and monitoring systems. This highlights how machine learning, deep learning, and hybrid AI architectures, such as transformer-based and ensemble models, enhance real-time multisource environmental data analysis encompassing satellite images, IoT sensor networks, and meteorological records. Two hybrid models, BREATH-Net and CATALYST, were used as case studies for NO¿ and PM¿.¿ prediction in city centers, demonstrating significant improvements in predictive power and operational scale. This chapter also introduces emerging technologies such as digital twins, foundation models, and edge AI for decentralized, real-time environmental intelligence. Ethical and governance principles, such as explainable AI and federated learning, are highlighted to promote transparency, privacy, and accountability. This detailed outline illustrates how AI-based systems can support policymaking, facilitate sustainable urban governance, and enhance global environmental resilience.
