Firefighting operations in hazardous environments demand agile and adaptive strategies to effectively combat fires while safeguarding the lives of firefighters and civilians. Traditional approaches often rely on predetermined protocols, which may not adapt well to dynamic and unpredictable situations. This paper proposes a novel framework utilizing Reinforcement Learning (RL) to optimize rescue mission strategies. By harnessing the power of RL, this approach enables AI agents to learn and adapt their behavior based on environmental cues and feedback, leading to more effective and responsive rescue operations. The framework integrates various factors such as terrain and path complexity and the presence of hazards, like fire and smoke, into the decision-making process. Through iterative learning, agents evolve their strategies, identifying optimal paths and rescue tactics. Furthermore, this proposal discusses the potential benefits of employing DRL in rescue missions, including enhanced adaptability, scalability, and robustness in diverse and challenging environments. The adoption of RL to optimize strategies for rescue missions represents a significant opportunity to progress in the disaster response domain. At the end, our results show that the RL-driven method enhances rescue operation outcomes and safety by reducing rescue times, increasing the number of people rescued, optimizing resource utilization, and boosting overall efficiency.