The rapid growth in electronics has led to an unprecedented amount of electronic waste (e-waste), which has raised serious concerns for the environment and public health globally. This has necessitated the implementation of effective e-waste management, which has called for innovative methods to improve recycling and disposal processes. A thorough examination of artificial intelligence (AI) and image processing techniques and their applications in e-waste management is presented in this review paper. The latest developments in AI and image processing methods, aimed at automating the identification, classification, and reuse of e-waste, are analyzed. The practical benefits and challenges of incorporating AI and image processing into e-waste recycling processes are illustrated through case studies and real-life scenarios. These revolutionary techniques have the potential to transform e-waste management by enabling precise identification and categorization of e-waste components, increasing the recovery rates of valuable materials, and minimizing ecological impacts. Despite their potential, the implementation of these advanced technologies is complex and faces many challenges. The study investigates these challenges and assesses how recent advancements can further improve environmentally friendly e-waste management. Its aim is to provide a useful resource for applying AI and image processing to reduce the environmental impact of e-waste.