One of the successful ways of overcoming the restrictions of wireless sensor networks is the integration of drone swarms, especially those of autonomous drones. The present work offers a new conceptual design, a futuristic idea which uses Deep Multi-Agent Deterministic Policy Gradient (MADDPG) to facilitate the movement of drone swarms by better planning their paths and also by gaining cooperative navigation in wireless sensor networks, thus ultimately resulting in reduced energy consumption and enhanced network lifetime. There is a fundamental difference between the approach proposed here and other traditionally centralized approaches. Each drone can individually figure out and change its course through decentralized decision-making, at the same time coordinate with the rest of the agents to maintain the overall synchronization. Soak a battery with deep reinforcement learning, the network can function smarter in adjusting its needs to communications interruptions, variations in the nodes, terrain, etc. In essence, the key idea here is the method's capacity to maximize swarm-level interaction augmented by machine learning that can reliably and energetically tackle the problem of hardware restrictions. As a result, it also opens up the potential for more drones to be naturally assimilated into the swarm, thereby increasing scalability and without the need for progressive redesigns of the control mechanism. The current work showcases the exciting benefits that can be unlocked by marrying multi-agent reinforcement learning with the WSN architecture.