Advancements in Waste Segregation Through Machine Learning and Integrating AI for Sustainable Waste Management

Management of waste is very important for sustainable development, mostly in urban areas where population growth and industrial activity are on the rise leading to the generation of huge volume. Proper segregation and collection of waste are essential because it helps in recycling and reduce the amount that piles up as a dump, thus saving several resources. This paper reviews the state of the art in waste segregation methodologies and how different approaches are employed to segregate wastes using machine learning algorithms and robotics use-case scenarios with Internet of Things (loT) devices. This paper offers a systematic review synthesising the key elements of segregation in recent literature, comparing datasets for research and examining various ways to measure it. In addition, we suggest improvements to existing systems by the incorporation of multi-modal data processing and real-time adaptive methods. Our results indicate that there have been some major advances in terms of graph processing, but building and deploying a real-time system is still too difficult. The paper concludes with recommendations for future research directions, emphasizing the need for more robust datasets and the integration of AI-driven adaptive systems for dynamic waste management.