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The increasing need for accurate, accessible waste management data has made efficient waste collection tracking essential for modern smart cities. This project presents Clean Sphere AI: Sensor-Guided Waste Monitoring & Decision System, a secure web application built entirely with Java (backend) and HTML+ CSS (frontend) for tracking, managing, and visualizing real-time waste metrics. The system centralizes container records, allows municipal staff to log collection events and maintenance reports, and provides chronological fill-level trends and search capabilities to retrieve relevant past events quickly. Core functionality includes role-based access control, audit logging of edits, and automated flagging of critical events (e.g., overflow alerts vs. scheduled collections). The backend, developed using Java (Servlets or Spring Boot), handles authentication, data validation, and business logic; it also exposes RESTful endpoints consumed by a responsive HTML/CSS frontend. Data is persisted in a relational database (SQLite/MySQL) with an efficient schema optimized for time-series queries. The design emphasizes privacy, integrity, and simplicity: only authorized users can view or modify records, and all changes are tracked. Key features include geospatial visualization of container status, quick keyword search, exportable reports (CSV/PDF), and a module for predictive risk flags using waste-level heuristics (e.g., overflow prediction). The system is modular to allow future integration of machine learning models or GIS/logistics information systems. Built with commonly taught technologies, this project demonstrates practical software engineering, secure data handling, and user-centered design suitable for municipal workflows