In this work, we introduce a comprehensive garden waste management system, distinguished by three innovative approaches aimed at enhancing the efficiency and sustainability of garden waste processing. First, we significantly broaden the range of detectable waste types through the development of a deep learning model, trained on an enriched dataset featuring both conventional and synthetic images. This model’s capability to recognize a diverse array of garden waste components marks a pivotal advancement in waste classification technology. Second, we integrate real-time classification and sorting mechanisms, employing Internet of Things (IoT) devices and robotic automation. This integration not only streamlines the waste management process but also ensures that waste is sorted with unprecedented accuracy and speed, directly at the point of disposal. Third, the application of cutting-edge deep learning architectures, propels the system’s classification performance to new heights. Our experimental results demonstrate the system’s superior performance in accurately classifying and efficiently sorting garden waste, highlighting its potential to revolutionize current waste management practices.