Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cans, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average precision (mAP50) of 86%. The vision system provides real-time classification, while the PLC manages conveyor and actuator operations to ensure seamless sorting. Experimental results in a controlled environment validate the system’s high accuracy, minimal processing delays, and scalability for industrial recycling applications. This innovative integration of AI vision with PLC automation enhances sorting efficiency, reduces ecological impacts, and minimizes labor dependency. Furthermore, the system aligns with sustainable waste management practices, promoting circular economy principles and advancing the Sustainable Development Goals (SDGs).