Advancing Circular Economy Through AI-Driven E-Waste Management: A Comprehensive Review of Current Research, Challenges, and Future Directions

Electronic waste (e-waste) is one of the fastest-growing waste streams worldwide, posing critical environmental, economic, and public health challenges. The circular economy paradigm offers a holistic approach to managing e-waste through resource recovery, recycling, and reduced landfill disposal. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated transformative potential in addressing central bottlenecks in e-waste handling, including precise materials identification, automated disassembly, improved recycling efficiency, and predictive logistics. This paper critically evaluates 30 peer-reviewed studies published between 2010 and 2025, selected via a transparent screening process, focusing on AI- and ML-driven technologies for e-waste management within the circular economy. We synthesize evidence from real-world implementations, discuss performance metrics (e.g., sorting accuracy, throughput gains, and carbon footprint reduction), and highlight how AI and ML algorithms can boost recovery of high-value materials, reduce environmental impact, and improve overall cost-effectiveness. We further examine current trends, underscore notable achievements, and analyze key challenges—such as data privacy, regulatory gaps, heterogeneous waste streams, and algorithmic bias. A series of policy recommendations and a future research roadmap are proposed, delineating technological, regulatory, and socio-economic pathways to expedite adoption of AI-enhanced e-waste management. By presenting a rigorous, thematically focused synthesis, this review anchors AI-based e-waste solutions as a linchpin for advancing the circular economy and achieving sustainable development.