Intelligent Waste Management Systems: A Review of IoT, Deep Learning, and Optimization Techniques for Sustainable E-Waste and Solid Waste Handling

Abstract

This review addresses the urgent environmental and health issues posed by the rapid growth of electronic waste (e-waste) and municipal solid waste (MSW), highlighting the role of emerging technologies in crafting sustainable waste management solutions. It explores the integration of the Internet of Things (IoT), deep learning, and optimization algorithms in enhancing waste classification, recycling, and disposal. Key innovations include IoT-enabled smart bins for real-time monitoring, deep learning models like CNNs achieving up to 97% sorting accuracy, and optimization techniques that improve energy efficiency and scalability. The paper synthesizes findings from over 50 studies and emphasizes both technical advances and implementation challenges, such as data limitations, model interpretability, and the lack of robust policy frameworks. Future research directions include explainable AI, edge computing, and global standardization of e-waste regulations. The review is intended for researchers, developers, and policymakers working toward circular economy principles and sustainable smart city solutions.