Apply AI and computational methods to real-world pollution challenges Monitoring and mitigating pollution across air, water, and soil systems demands computational approaches that can handle complex, uncertain environmental data. AI for Pollution Management presents interdisciplinary frameworks combining machine learning, deep learning, fuzzy logic, soft computing, and multi-criteria decision-making approaches. This edited volume delivers both conceptual models and case studies that connect theoretical methods with actionable pollution management strategies. The book details the integration of IoT, smart sensor networks, remote sensing, edge and cloud computing, explainable AI, and emerging computational approaches for intelligent environmental monitoring and decision support. Contributors demonstrate practical approaches for implementing smart pollution monitoring systems and provide decision-making frameworks to support sustainable environmental management and policy. Across the volume, conceptual frameworks, technical methodologies, and applied case studies address air-quality prediction, water-quality assessment and contamination detection, soil-pollution monitoring, industrial pollution control, emerging contaminants, and sustainable waste management. The book also covers: AI and machine-learning approaches for pollution detection, prediction, forecasting, and environmental decision support MCDM frameworks applied to prioritize pollution interventions and evaluate environmental remediation strategies across competing criteria IoT-enabled smart sensor networks, edge computing, and AI-based systems for real-time environmental monitoring Hybrid quantum-classical algorithms and emerging quantum-computing approaches for addressing carbon-emission problems Soft computing techniques including fuzzy logic for handling imprecise and uncertain environmental information AI applications in circular economy, industrial pollution control, waste recycling, and sustainable environmental management AI for Pollution Management serves environmental scientists, computational researchers, academics, and sustainability professionals who need rigorous and practically relevant computational approaches for pollution monitoring and mitigation. Industry decision-makers seeking to strengthen environmental performance through data-driven strategies will also find directly applicable frameworks and case studies throughout the volume.