Artificial Intelligence in Circular Economy and Waste Management: A Systematic Review of Applications for Optimization Strategies and Resource Recovery Pathways

Background: The transition from conventional waste management to circular economy systems has generated more interest in how artificial intelligence (AI) can enhance waste prevention, sorting and recycling, optimization, and resource recovery. Owing to the growing complexity and data-driven nature of waste systems, AI has been used to aid operational and strategic decisions throughout the waste management chain. Methods: This study is a systematic review of the literature between 2016 and 2026 on AI in waste management in the circular economy, conducted according to the PRISMA 2020 guidelines based on three directions: AI application, optimization techniques, and recovery routes of resources. In this systematic review, 52 studies were included through systematic database searches, eligibility screening, clear inclusion criteria, and thematic synthesis of the literature to identify the key research areas of AI techniques, optimization strategies, and resource recovery pathways in the circular economy and waste management. Results: The review shows that optimization is the key to implementing the principles of the circular economy in waste management decision-making, and that AI is no longer seen as a taboo topic in the literature but as a functional application. System-level integration and the integration of systems in a holistic circular economy are not yet fully consolidated. To overcome the limitations of single-application studies, an Integrative AI–Circular Waste Value Chain (AI-CWVC) framework is proposed. Conclusions: This review highlights the importance of AI in supporting circular waste management and points to the need for further research that is more integrated, evidence-based, and policy-aligned.