Reliable communication is one of the main challenges in disaster scenarios, where conventional infrastructure is often unavailable and mobile nodes exhibit highly dynamic behavior. This paper presents an artificial intelligence–based approach to enhance communication in wireless ad hoc networks under such conditions. A dataset was generated from scratch by integrating the ns-3 network simulator with BonnMotion to model realistic human mobility in disaster environments. From these simulations, two key features—channel utilization factor and queue packet size—were extracted and used to train a supervised learning model with the CatBoost algorithm. The model was validated with accuracy, precision, and F1-score, and then reintroduced into the simulation to support real-time decision-making. Experimental results show that the AI-enhanced strategy achieves substantial improvements in Packet Delivery Ratio (PDR), Throughput, and End-to-End Delay compared to a baseline without Quality of Service (QoS). These findings demonstrate the feasibility of integrating machine learning into communication layers to increase the resilience and efficiency of ad hoc networks for disaster response applications.