In this work, we have proposed a model that can detect emergency cars on a heavy traffic road. A populated region like Kurdistan faces too much traffic on the road and because of that emergency car like ambulance and fire-service fall into trouble middle of the road. Our model will solve this problem. It can be embedded with CCTV to track emergency can and give priority in that road to pass the emergency can. With this automated process, no human effort will be required to manually help such scenario. In our project we used a customized Yolov5 object detection algorithm. YOLO an acronym for (You Only Look Once), it is an object detection algorithm that divides images into a grid system. Each cell within the grid is responsible for detecting objects within itself. YOLO models are used for Object detection with high performance which consists of 84 classes to detect and differentiate between 84 different objects. Our model is based on 4 classes which are (Firetrucks, Ambulance, Police Car, and Normal Cars) classes. Our model has achieved impressing results in detecting and identifying emergency cars of all kinds, for Police Cars we have the result of 98%, for Fire Trucks 96%, for Ambulances we’ve got 89% and for Normal Cars 97% results.