Emergency Medical Service (EMS) stands as a crucial lifeline within any nation, given its pivotal role in timely life-saving interventions. However, the effectiveness of EMS hinges greatly upon the concept of the “golden hour,” requiring patients to receive care swiftly. This presents a significant challenge, particularly within the context of developing countries. The responsiveness of an EMS is intricately linked to factors such as the location of EMS bases, ambulance distribution among them, and overall operational management. To address the issue of reducing EMS response times, this paper proposes an operational solution centered on ambulance dispatch and relocation, leveraging deep reinforcement learning (RL) agents trained in a straightforward yet comprehensive simulated environment. An analysis comparing various reinforcement learning agents using Q-learning was conducted, demonstrating improved efficacy and performance in ambulance relocation by over 54%.