This paper presents a novel framework for real-time object detection in Wireless Mesh Networks (WMNs) using AI driven drones. The system integrates YOLOv5 for object detection, a hybrid greedy and reinforcement learning-based routing algorithm for efficient data communication, and a scalable multihop mesh topology. MATLAB-based simulations evaluate energy consumption, detection accuracy, and network connectivity, showcasing significant performance improvements. The results demonstrate the system's ability to detect objects accurately, maintain robust connectivity, and optimize energy consumption, even in dynamic environments. Future work includes expanding the system to integrate 5G and satellite networks and conducting field tests.