<![CDATA[The transition to a zero-waste economy necessitates innovative approaches to circular manufacturing, where Artificial Intelligence (AI) plays a pivotal role. This study examines how AI technologies—including predictive maintenance, machine learning, and blockchain—enhance resource efficiency, reduce waste, and optimize supply chains in circular manufacturing systems. Employing a qualitative methodology, the research synthesizes literature from peer-reviewed journals and industrial case studies to analyze AI's applications across product design, production, and end-of-life processing. Findings reveal that AI-driven solutions significantly improve material recovery, operational transparency, and demand forecasting, yet face hurdles such as high costs, data quality issues, and energy demands. The study proposes policy-industry collaboration and advanced technologies like digital twins to overcome these barriers. Implications suggest that AI integration not only accelerates sustainability goals but also fosters economic resilience, as evidenced by reduced emissions and extended product lifecycles. This research contributes a framework for scalable, AI-enabled circular manufacturing, addressing gaps in existing literature while highlighting future directions for innovation in sustainable industrial practices.]]>
