Waste Management using AI: Optimizing Sustainability through Innovation

Abstract. There has been an increasing need for effective, sustainable, and scalable waste management systems due to the rapid increase in global waste generation. This comprehensive review intersects Artificial Intelligence (AI) with municipal solid waste management (MSWM) through the lens of 25 selected publications from the years 2018 to 2024. The review illustrates how AI has transformed waste forecasting, smart bin monitoring, route optimization, robotic waste sorting, and real-time decision making. In examining the core AI techniques machine learning, deep learning, computer vision, and hybrid models, the review places these techniques within the context of the waste life cycle—beginning with generation, through processing, to disposal. Moreover, it looks at integrated frameworks like SWM 4.0 where AI is combined with Industry 4.0 technologies, including the IoT, big data, and even blockchain. The results stress AI's ability to optimize operational activities, mitigate negative environmental effects, and facilitate concrete policy decisions. However, issues related to data quality, system incompatibility, and ethics pose challenges to realizing such opportunities. This review evaluates existing research on AI-based smart systems and sets forth a research agenda aimed at advancing circular economy objectives and fostering sustainable urban frameworks.