Effective waste disposal is a critical challenge for nations worldwide. India alone generates approximately 65 million tons of waste annually, presenting significant difficulties for traditional waste management systems. This study proposes LIXO, an innovative waste management platform that integrates location-based public reporting, machine learning-based waste classification, and optimized collection routing. Through a user-friendly web interface, residents can submit geo-tagged images to report the status of waste bins. These images are processed using Convolutional Neural Networks (CNNs) to classify waste types and estimate bin fill levels. While CNNs are powerful for image classification, their performance can be affected by factors such as varying illumination, occlusions, and cluttered backgrounds, as well as challenges in detecting small or detailed features. LIXO addresses these limitations with advanced filtering techniques that prioritize bins requiring urgent collection. Optimized routing algorithms then schedule waste collection efficiently, reducing operational costs and environmental impact. The backend system provides centralized oversight and data management, aligning with smart city objectives for transparent and sustainable waste systems. Pilot studies demonstrate that LIXO improves collection efficiency, fosters community participation, and offers a cost-effective, practical, and low-intervention solution to urban waste management challenges