• Mr. Sandeep Kumar Mude
• Mohanthi Koyi
• Venkatesh Badarala
• Sri Satya Sai Jayakrishna Chittiboina
• Tagore Ambati

The growing volume of waste generated globally poses significant environmental challenges, demanding innovative solutions for efficient waste management. Recent advancements in machine learning (ML) have the potential to revolutionize the way we handle waste, transforming it from a growing problem into a resource. This paper explores the role of ML technologies in waste management, focusing on applications such as waste classification, recycling optimization, and waste-to-energy systems. By leveraging techniques such as image recognition, natural language processing, and predictive analytics, ML can enhance sorting efficiency, reduce contamination, and increase recycling rates. Furthermore, ML-driven systems can predict waste generation patterns, optimize waste collection routes, and contribute to sustainable practices by enabling smarter resource recovery. The paper highlights case studies where ML solutions have been successfully implemented, discusses the challenges faced, and outlines the future potential of ML in creating more sustainable and circular waste management systems. Through the integration of ML, the shift from "trash" to "treasure" becomes increasingly achievable, promoting a cleaner, more sustainable future.

Machine Learning, Waste Management, Recycling Optimization, Waste Classification, Waste-to-Energy, Predictive Analytics, Image Recognition, Natural Language Processing, Sustainable Practices, Resource Recovery, Circular Economy, Smart Waste Collection, Environmental Impact, Waste Sorting.

"Trash to Treasure: The ML Revolution in Waste Management ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.h13-h18, March-2025, Available :http://www.jetir.org/papers/JETIR2503702.pdf

"Trash to Treasure: The ML Revolution in Waste Management ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. pph13-h18, March-2025, Available at : http://www.jetir.org/papers/JETIR2503702.pdf