The rapid generation of waste globally has posed significant challenges to sustainable waste management practices. Traditional waste segregation methods are often manual and inefficient, leading to environmental degradation. This paper introduces the Smart Recycling Assistant, a novel web-based application integrating advanced AI models for waste classification, user feedback systems, and geolocation-based eco-friendly recommendations. The backend architecture employs a Flask framework to ensure seamless interaction between users, the AI model, and the database. A Convolutional Neural Network (CNN) forms the fundamental part of the waste classification system, offering high accuracy in identifying waste types. Geolocation integration suggests nearby recycling facilities, and a feedback loop enhances the AI’s accuracy over time. The system’s comprehensive approach, the technical methodologies, and mathematical formulation elaborately present both the problem and solution. Unlike traditional waste management systems, this project bridges the gap between AI-driven classification and actionable sustainability efforts, creating a pipeline from user input to eco-conscious decision-making. The Smart Recycling Assistant aims to simplify the waste management process while promoting greater environmental awareness. By combining innovative technology, creative reuse options, and community engagement, the platform encourages users to recycle, reuse, and reduce waste, contributing to a cleaner and more sustainable planet. This project highlights the potential of digital solutions in addressing serious environmental challenges, demonstrating how technology can be leveraged to promote eco-friendly practices and encourage long-term sustainability in everyday life.