<p>The increasing use of electronic products has led to a steady rise in E-waste worldwide. This is creating serious environmental, economic, and social concerns. Addressing this issue requires technology-enabled approaches that efficiently manage all stages of an E-waste product life cycle. This systematic review examines recent intelligent and computational techniques for E-waste management practices and discusses their effectiveness in improving planning, collection, segregation, and classification processes. This review is framed around five research questions (RQ1–RQ5). It examines developments in logistics and monitoring, efforts to reduce reliance on the informal recycling sector, and approaches aimed at improving material recovery for reuse and remanufacturing. The use of artificial intelligence (AI)-enabled algorithms, including machine learning (ML), deep learning (DL), computer vision (CV), Internet of Things (IoT), and blockchain, shows that they can contribute to the improvement of the decision-making process, increase the rates of resource recovery, and advance the principles of the circular economy. However, data quality constraints, scalability issues, and the necessity of cross-disciplinary cooperation do not allow widespread implementation, which hinders the potential benefits of computational technologies in enhancing E-waste management practices. The result of this review provides an in-depth discussion of the development of computational techniques used at different stages of E-waste management. It also determines the major challenges and future research directions. The paper combines the knowledge from interdisciplinary literature and highlights the opportunities of AI-based and data-driven solutions to a circular economy and encourages sustainable E-waste management practices.</p>