Closing the loop: A systematic review of artificial intelligence in circular e-waste management

The proliferation of technological advancements, knitted with volatile consumption patterns and poor end-of-life management of discarded electronics, is currently outpacing sustainability transitions, putting increasing strain on finite material resources and heightening ecological vulnerability. This, in turn, has made electronic waste a stealth contributor to climate change with adverse impacts on the environment, economy, and society at large. This reality underscores the urgent need for a strategic shift from linear waste-disposal methods to circular pathways, where Artificial Intelligence (AI) can build more sustainable feedback loops. At the nexus of AI and circular e-waste management, this study systematically reviews 147 articles from 2019 to October 2025. The analysis reveals a steady increase in AI adoption, particularly in deep learning-based detection and classification applications. To structure the evidence from the literature, a six-tier taxonomy is proposed, encompassing AI methods, lifecycle stages, data, waste types, limitations, challenges, and future pathways and opportunities. Beyond technical interventions, systemic and operational barriers that demand strategic levers to address regulatory ambiguities, legislative gaps, managerial inefficiencies, and logistical fragmentation are elucidated. These challenges underpin data availability and generalizability, as well as the lack of standardization, interoperability gaps, and barriers to the ethical and regulatory adoption of AI. In practice, these constraints limit the development of uncertainty-aware electronic waste systems capable of functioning under realistic operational dynamics. To this end, the paper reframes AI-based systems from terminal sinks to regenerative loops, aligning technological progress with sustainable electronic waste management.