Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Anuram Krishna, E.S.; Nanjundan, Preethi; Thomas, Lijo Title A name given to the resource Machine Learning Base Model for Waste Management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived A Circular Economy for Manufacturing Waste Management: Pathway Towards Sustainability;pp.267-290 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105024655260?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024655260?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anuram Krishna E.S., Department of Global FinancAnalytics, Christ University, India; Nanjundan P., Department of Data Science, Christ University, India; Thomas L., Department of Psychology, Christ University, India Description An account of the resource Waste management is an important global challenge, where increasing urbanization and industrialization lead to high waste production. Traditional waste landfill methods, including manual sorting and fixed collection plans often result in disabilities, increased costs, and environmental decline. Integration of machine learning (ML) and artificial intelligence (AI) in waste management systems provides a transformative approach to solve these problems. This chapter examines the role of ML in automatic waste sorting, future indication analysis, collection passport optimization, and converting wasteto-energy. Case studies from Singapore, San Francisco, and large waste management companies highlight MLs real-world applications to create smart and more sustainable waste management systems. Singapores AIoperated system and San Franciscos policy-based waste management models show MLs ability to adapt to comparative analysis resources and reduce landfill addiction. The findings show that AI-controlled waste management leads to high recycling speeds, reduces greenhouse gas emissions, and more efficient resource allocation. Despite challenges such as high implementation costs, regulatory concerns, and privacy problems, the future of waste management lies in AI-operated automation, blockchain integration for waste tracking, and AI-cleaned waste solutions. 2025 River Publishers. Subject The topic of the resource Artificial Intelligence; Sustainable Waste Management; Waste Managment Publisher An entity responsible for making the resource available River Publishers Relation A related resource ISBN: 978-874380688-2; 978-874380689-9; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online