Abstract

The fast expansion of electronic gadgets has led to a large increase in electronic waste (e-waste), causing environmental and health issues internationally. Traditional e-waste recycling processes generally struggle with efficient sorting and processing due to the complex and heterogeneous nature of electronic items. This project addresses the integration of Artificial Intelligence (AI) and Machine Learning (ML) approaches to boost e-waste sorting and recycling efficiency. By utilizing advanced picture identification and classification algorithms, AI-driven systems can effectively identify and separate diverse e-waste components, ultimately enhancing recycling rates and minimizing environmental impact. This study evaluates current AI applications in waste management, offers a framework for AI-enhanced e-waste sorting, and discusses the possible benefits and obstacles involved with integrating such technologies in recycling processes.