The Fogg Obstacle Recognition and Avoidance (FOGG) system is a novel approach that promises to improve drone navigation in adversarial environments. The system combines fog computing with the Adaptive Multi-Drone Fusion Path Optimization (AMFPO) algorithm to address significant challenges, including real-time obstacle detection, optimal path planning, and efficient coordination of multiple drones. Thus, fog computing is the core of this system, with decentralized and real-time processing of the obstacle data. This decentralized processing approach reduces latency since immediate decisions have to be taken in dynamic environments with unforeseen obstacles. Unlike cloud-based approaches, fog computing provides quick response times and system dependability even when there is low connectivity in the network. AMFPO optimizes dynamic flight paths by reinforcing the framework further. It takes into account drone energy expenditure, collision avoidance, and synchronization across multiple drones to enable it to operate effectively. AMFPO recalibrates routes with sensor data and real-time environmental factors. This is a highly effective framework in static and dynamic obstacle scenarios. The FOG framework has been tested exhaustively in simulation studies and real-world experiments, further ensuring robustness and high-performance efficiency. Overall, the key performance metrics-success rate, path efficiency, collision avoidance, and energy consumption-are significantly improved compared to traditional navigation strategies. Such properties make FOG an optimal solution for critical applications like search-and-rescue missions, autonomous delivery systems, and aerial surveillance, where precision, adaptability, and dependability are required.