U. Satya Narayana, B. Poojitha, Sk. Asiff, Dr. P. Nagendra Kumar 02 04 Effective waste management is essential in India to ensure environmental sustainability and safeguard public health With rapid urbanization and a growing population the volume of waste generated has increased significantly placing immense pressure on traditional disposal systems This has led to environmental pollution ecosystem damage and various health risks Conventional waste management methodssuch as manual sorting rulebased categorization and exporting wasteare increasingly proving to be inadequate Manual sorting is timeconsuming laborintensive and prone to human error while rulebased systems lack the flexibility to adapt to varied and changing waste types Exporting waste on the other hand raises serious environmental and ethical concerns due to potential mishandling To address these issues this research leverages a largescale image dataset containing millions of waste item images to build and assess robust waste classification models It utilizes MobileNetV2 and a lightweight convolutional neural network CNN to develop an AIdriven system capable of accurately classifying different types of waste This intelligent classification system can be integrated into smart bins and recycling infrastructures to automate the sorting process ultimately improving recycling efficiency and minimizing environmental impact Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026