Unmanned Aerial Vehicles play a crucial role in various operations especially where human life must be protected. This work presents an adaptable intelligent system suitable to enhance the efficiency and effectiveness of drone swarm operations in a 3D dynamic environment. The system incorporates several modules, including an Ant Colony Optimization (ACO)-based path planning algorithm, collision avoidance mechanism, messaging system, and a hybrid navigation approach, which evaluates the application requirements to decide to prioritize the desired formation of the swarm or the path length and flight time. The proposed system is adaptable and can optimize to several optimization parameters, including solution quality, time consumption, mission completeness, and average divergence. The experiments show that the system consistently provides high-quality paths, achieving around 97% path quality in most cases, and never declines below 90%, even in challenging scenarios. The collision avoidance module ensures 100% mission completeness successfully navigating drones around obstacles and maintaining an optimal path. Moreover, the hybrid navigation approach demonstrates the ability to maintain desired formations while dynamically adapting to obstacles. The systemâ\euro™s performance shows its potential for real-world applications, ensuring efficient and autonomous operations in different missions.