Artificial intelligence in waste management systems: Applications, challenges, and prospects

Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8% by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based material classification. Beyond summarizing existing approaches, it proposes a multilayer framework that connects data sensing, planning, sorting, and treatment within an adaptive lifecycle perspective. The review further examines existing gaps related to policy support, infrastructure readiness, data standardization, and scalability. While numerous studies demonstrate the potential of AI to improve operational efficiency and material recovery, real-world implementation remains limited by economic, regulatory, and technological barriers. To address these limitations, a strategic roadmap is presented, integrating technical innovation with governance and investment pathways to enhance implementation potential. By consolidating these insights, the study offers a comprehensive synthesis that clarifies how AI can strengthen circularity across the waste lifecycle and guide the sector toward scalable, evidence-based sustainability transitions.