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Managing waste efficiently has become a major issue in rapidly growing urban areas, creating the need for smart, automated systems that minimise manual effort and improve recycling processes. This project introduces an IoT-driven real-time waste classification and monitoring system that combines sensors, embedded hardware, and machine learning to identify and sort waste automatically.A camera module connected to an IoT- enabled microcontroller or single-board computer—such as a Raspberry Pi—captures images of disposed items. These images are processed using a lightweight deep- learning algorithm that categorizes waste into groups like biodegradable, non-biodegradable, plastic, metal, and paper. To complement image-based classification, ultrasonic and load sensors track the bin’s fill level and weight, ensuring instant alerts whenever the bin approaches full capacity. The system transmits classification results and bin status to a cloud platform through Wi-Fi or MQTT, enabling real-time monitoring, analytics, and automated notifications. This approach improves segregation accuracy, reduces the amount of waste directed to landfills, and supports sustainable smart-city waste management initiatives. The overall solution is affordable, easy to scale, and capable of enhancing waste collection and recycling efficiency. By automating segregation and continuously observing bin conditions, the IoT-based system lowers human involvement, boosts recycling performance, decreases operational costs, and contributes to cleaner urban spaces. Its flexible design makes it suitable for residential areas, institutions, public locations, and large smart-city environments. The research confirms that integrating machine learning with IoT substantially improves the speed and accuracy of waste classification while promoting environmental sustainability