📄 Abstract
Efficient management of municipal solid waste is vital for public hygiene, environmental sustainability and the sensible use of civic resources. Conventional collection practices rely on fixed schedules and manual supervision, which leads to overflowing bins, slow response, needless collection trips and wasteful use of resources. Prior work has presented IoT-based smart bins, AI-based waste classification, predictive analytics, GIS-based analysis and citizen-oriented solutions, yet these capabilities are usually built as isolated systems. This paper proposes an integrated intelligent framework that combines IoT-based waste monitoring, AI/ML-based waste classification, waste-collection forecasting, GIS visualization, full-bin alerts and waste-related reporting. The framework gathers and analyses bin fill level, weight, location, timestamp, historical waste patterns and waste images, and presents the results on a centralized web platform that supports data-driven collection decisions. A prototype is developed to show how IoT, artificial intelligence, predictive analytics and spatial information can be combined into a practical municipal waste-management solution.
🏷️ Keywords
📚 How to Cite:
Shyama Devi, Dr. Prof. S. Budhkar, Priyanka Abhang, Rohit Ambdekar, Shivam Lendghar , WASTEVISION: AN INTELLIGENT IoT AND AI-BASED FRAMEWORK FOR PREDICTIVE AND OPTIMIZED MUNICIPAL WASTE MANAGEMENT , Volume 12 , Issue 11, November 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 17 - 23 , DOI: https://doi.org/10.36713/epra32054