This paper introduces a novel deep learning-based approach using YOLO (You Only Look Once) for real-time object detection of medical instruments in the healthcare field. We focused on detecting crucial medical objects, including stethoscope, digital thermometer, blood pressure gauge, and robotic surgery tools. To develop a robust model, we curated a diverse dataset of medical object images with meticulous annotations. YOLOv3 was chosen for its real-time capabilities and ability to handle multiple object classes. We train our model on a dataset of images that contain these objects. We evaluate our model on a test dataset and show that it can achieve high accuracy. We also show that our model is able to detect objects in faster time. The model was fine-tuned and optimized using a state-of-the-art optimizer, trained on a powerful GPU for efficient convergence and precision. Evaluation on a separate test dataset demonstrated outstanding accuracy in detecting medical instruments under various scenarios. Medical object detection addresses these challenges by automating the process of locating and recognizing medical instruments. The paper also presents a thorough analysis of strengths and limitations, comparing it with traditional methods and other deep learning approaches and highlighting the superiority of our proposed YOLO-based solution. Overall, this work has significant potential to enhance medical device localization, streamline surgical procedures, and improve patient care in the healthcare industry. The YOLO model achieved an impressive accuracy of 94%. The high precision of 89% e YOLO model achieved a remarkable recall of 88%, indicating that it successfully identified a significant portion of the true positive objects.