Integrating artificial intelligence and Internet of Things for solid waste management: a review

Abstract

The increasing pace of urbanization and industrial growth has intensified the challenges of solid waste management, demanding intelligent, data-driven, and sustainable solutions. This review explores how the combined application of artificial intelligence (AI) and the Internet of Things (IoT) is revolutionizing conventional waste management practices into intelligent, automated, and responsive systems. Through a comprehensive review of 43 scholarly publications, case analyses, and technical studies, this paper emphasizes how AI-based methods—such as learning algorithms, image recognition, and data-driven prediction—improve waste sorting precision, recycling performance, and material recovery efficiency-enhance waste segregation accuracy, recycling efficiency, and resource recovery. Simultaneously, IoT-based systems employing sensors, cloud platforms, and smart bins enable real-time waste monitoring, dynamic routing, and optimized collection logistics. Emerging technologies like blockchain for waste traceability, robotics for automated sorting, and advanced analytics for decision-making are also examined. Despite these advancements, challenges related to scalability, interoperability, cost, and data privacy persist. This review identifies current research gaps, proposes future directions, and emphasizes the importance of integrating AI and IoT with circular economy principles under Industry 5.0 to achieve sustainable, efficient, and human-centric waste management solutions.