Global circularity: 7.2%, 21% lower than 5 years ago. Incremental approach (recycling → reuse → prevention) is more feasible than radical systemic transformation for SMEs.

Synthesis of global evidence: significant mitigation potential, but impact scale depends on integration into climate policies and technological support.

From 264 AI-mentioning materials papers in 2014 to ~10,000 in 2024. Self-driving robotic labs compress the digital→physical cycle. Mandatory pairing: AI + innovative business models + collection infrastructure.

Comprehensive narrative review of peer-reviewed literature published between 2015 and 2025, covering Web of Science, Scopus, IEEE Xplore, ScienceDirect and Google Scholar. Critical evaluation of AI approaches potential and limitations across the entire waste management lifecycle confirms that AI technologies — intelligent sorting systems, predictive models, complex decision automation — radically transform resource recovery and environmental impact reduction. Technical, economic and systemic barriers remain significant for large-scale adoption.