The surge in medical waste due to global health crises and increased medical activities has introduced significant environmental challenges, adversely affecting both land and ocean ecosystems. Managing this waste effectively is critical to mitigating its environmental impact, and the adoption of advanced sorting technologies is essential to addressing these challenges. This paper introduces a novel sorting machine equipped with the You Only Look Once (YOLO) algorithm, specifically designed to classify and sort various types of medical waste, including facemasks, syringes, gloves, and sharp objects. The sorting machine utilizes the YOLO v8 model, which is integrated with an Internet of Things (IoT) system to control the sorting process. The model was meticulously trained on a dataset comprising 6,760 images, achieving impressive performance metrics, including a mean Average Precision (mAP) of 98%, a precision rate of 0.958, and a recall rate of 0.963. These results underscore the model’s accuracy and reliability in detecting, classifying, and sorting medical waste with high precision. The prototype developed in this study successfully implements the trained YOLO algorithm, demonstrating its capability to enhance the efficiency and effectiveness of medical waste management. By integrating AI-based algorithms, IoT technology, and mechanical components, this advanced sorting machine represents a significant step forward in addressing the environmental challenges posed by medical waste. It offers a practical solution that not only improves the sorting and disposal process but also contributes to the broader effort to protect ecosystems from the harmful effects of improperly managed medical waste.