Multiple Unmanned Aerial Vehicles (UAVs) have a greater potential to be widely used in civil and military applications. Swarm of UAVs can be deployed in a multitude of 24/7 security and surveillance. The network management and pattern formation are crucial for multi-UAV formation control mechanisms while cautiously navigating the surveillance areas. A Deep Reinforcement Learning (DRL) based Formation Flight Control for Navigation (FFCN) is used to efficiently build the UAV swarm, which decreases networking load by minimizing communication and processing involved in pattern formation. Moreover, through the leader-follower navigation, the network management of the swarm is substantially simplified. The leader-follower approach in FFCN is efficient for multi-UAV as the navigation system needs to find only the leader's trajectory. However, the failure of the leader due to actuator faults decreases the efficiency of the system. The proposed FFCN addresses the above by including a fault-tolerance mechanism, thus improving the system's reliability. Simulation results show that the FFCN model achieves faster convergence in less time with a lower collision rate. The model's usage reduced the collision rate to 3.4% in successful formation without colliding with other UAVs.