Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{187893,
        author = {Suneet Kumar and Akash Prakash Upadhyay and Rahul Singh},
        title = {Integration Of Artificial Intelligence in Municipal Solid Waste Management: Smart Segregation and Resource Recovery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7094-7105},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187893},
        abstract = {— Rapid urbanization, evolving consumption patterns, and the increasing complexity of municipal solid waste streams have intensified operational, environmental, and governance challenges in waste management systems. Conventional manual and mechanical sorting practices remain inefficient, labour-intensive, and prone to high contamination, resulting in low material recovery and continued landfill dependence. This study investigates the integration of Artificial Intelligence (AI)—specifically computer vision, machine learning, and hybrid sensor-based systems into municipal solid waste segregation and resource recovery. Using a mixed-methods approach that includes systematic literature review, laboratory experimentation, multi-scale field implementation, stakeholder consultations, and economic assessment, the study evaluates both the technical feasibility and institutional readiness for AI-enabled waste sorting.
Laboratory trials demonstrated that advanced deep learning models achieved up to 94.8% classification accuracy, yielding a 38% improvement over manual sorting. Field deployments across three facilities (small, medium, and large scale) revealed enhancements in throughput, reduced contamination, and operational cost savings averaging 28%, with payback periods under 1.7 years. Stakeholder analysis further identified regulatory ambiguity, limited workforce preparedness, and infrastructural constraints as key barriers to scaling AI solutions. Conversely, opportunities exist in circular economy integration, workforce upskilling, PPP-driven technology adoption, and policy frameworks that support innovation in waste systems.
The study concludes that AI-enabled smart segregation can significantly enhance resource recovery, reduce landfill load, and improve environmental sustainability when supported by governance reform, institutional capacity building, and strategic investment. The findings offer a scalable implementation roadmap for municipalities, policymakers, and technology providers seeking to modernize waste management systems and transition toward data-driven, resilient, and circular urban waste economies.},
        keywords = {Artificial Intelligence (AI); Municipal Solid Waste (MSW); Smart Segregation; Computer Vision; Machine Learning; Internet of Things (IoT); Resource Recovery; Waste Sorting Automation; Circular Economy; Economic Viability; SWM Rules 2016; Material Recovery Facility (MRF); Smart Waste Management; Environmental Sustainability; Policy Framework; Public–Private Partnerships (PPP).},
        month = {November},
        }