Improper segregation of municipal solid waste at the point of disposal is one of the principal reasons recycling rates remain low worldwide, since mixed streams contaminate otherwise recoverable materials. This paper presents the design and implementation of an AI-based Smart Waste Segregation Assistant: a fully client-side, browser hosted application that uses a pre-trained convolutional neural network (MobileNet, via TensorFlow.js) together with a rule-based keyword-to-category mapping engine to classify a live camera feed of a waste item into one of six actionable categories — Organic, Plastic, Paper, Glass, Metal, and Hazardous — and to recommend the correct disposal bin in real time. Unlike conveyor-belt or sensor-based industrial smart-bin systems that require dedicated hardware, cloud inference, or large custom-labelled datasets, the proposed assistant runs entirely on commodity devices (smartphones, tablets, laptops) with no installation and no image data leaving the user's device, and additionally allows an end-user to plug in a custom Teachable Machine model to raise classification accuracy without writing code. We review recent literature on CNN-based waste classification, edge-deployable models, IoT-integrated smart bins, and industrial-grade cluttered-scene datasets, and use the reported results to position the proposed system's trade-offs between accessibility, cost, privacy, and classification accuracy. Our analysis shows that while specialized, dataset trained systems achieve 89-99% accuracy under controlled or industrial conditions, they demand substantially higher deployment cost and infrastructure; the proposed lightweight assistant instead targets household- and communitylevel assistive segregation and environmental education, complementing rather than replacing industrial sorting pipelines. Limitations, comparative benchmarking against the surveyed systems

E Arvind Elumalai Udiyar, Dr Manoj Singh, "AI-Powered Smart Waste Segregation Assistant: A Lightweight, Browser Based Deep Learning System for Real-Time Waste Classification" International Journal of Scientific Research and Engineering Development, V9(5): Page(1110-1116) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.