Even though the number of cars on the road is increasing by the day, traffic congestion has become a serious problem with more vehicles on the road. Emergency vehicles are affected by congestion at a traffic light and are waiting with a large number of vehicles and traditional traffic light systems are currently unable to handle the increased traffic flow efficiently. Therefore, we investigated and came up with a prototype of a multi-module solution called Salek. First, finding the most efficient timing for the light changes over a series of intersections in a straight path with different speed limits. Second, a module that works on identifying emergency vehicles using image classification machine learning techniques. The goal of the first module is to find the most efficient traffic light synchronization to reduce the time of travel and overall gas consumption using graph algorithms. On the other hand, the second module guarantees the right of passage will always go to emergency vehicles when needed, using the YOLOv5 algorithm. Therefore, these two modules work together to find the most efficient scheduling and interruption as needed. Salek is able to reduce traffic overcrowding at signals by 50%, and the number of vehicles in the entire road (more than one intersection) by 42.8%. In addition, Salek can identify emergency vehicles and give the right of passage with an accuracy of 98%, and it can decrease the response time of emergency vehicles compared to a traditional system.