The abstract should summarize the contents of the paper in short terms, i.e. 150–250 emergencies of its patients. The system presented in this paper is an innovative method that optimally calculates the shortest path using shortest path finding algorithms. This smart system utilizes image pattern recognition to continuously monitor roads in both urban and suburban locations to interpret road conditions, ultimately leading ambulances around more congested streets to the hospitals. A very important component of this system is identifying the specifics of the emergency, such as the condition of the patient, how far the hospital is, and what medical next steps are available at which hospital. In contrast to typical GPS that relies on outdated maps or past traffic congestion patterns, the smart system only analyses real-time data which allows for the decision-making process of routing ambulances to occur cumulatively and instantaneously. The smart system uses OpenStreetMap API for live traffic data, and calls advanced algorithms like Dijkstra’s and A*, to determine the best route. By integrating these advanced systems, it is expected that the ambulance response times will increase in speed and efficiency; which is important in every second matters situations. The smart system has developed a solution that is flexible and advanced enough to be used in smart cities, with possible utilization of IoT traffic sensors, and blockchain technology to ensure the decisions are secure and accurate in the future. Ultimately, this new and innovative system assures ambulances will arrive at their patients faster, saving their lives in the process.