Smart Waste Collection Predictor: An IoT and Machine Learning-Based Approach for Predictive Waste Management

Traditional waste collection systems follow fixed schedules, which often result in overflowing bins, unnecessary collection trips, increased fuel consumption, and environmental pollution. This project proposes a Smart Waste Collection Predictor using Internet of Things (IoT) and Machine Learning technologies to improve waste management efficiency. The system uses an ultrasonic sensor (HC-SR04) connected to an ESP32 or Arduino Uno to monitor waste-bin filling levels in real time. The collected data is transmitted through Wi-Fi to a cloud platform such as Firebase or ThingSpeak. Python-based machine learning techniques are proposed to analyse historical filling patterns and predict future waste levels. A web-based dashboard developed using HTML, CSS, and JavaScript provides monitoring information, while LED or buzzer alerts indicate critical waste levels. The proposed system aims to prevent bin overflow, optimize collection schedules, reduce fuel consumption, and support clean and sustainable smart-city environments.