Wireless sensor networks (WSNs) play a crucial role in various real-time applications, including environmental monitoring, agriculture, and security. Target coverage optimization is a fundamental aspect of WSNs to ensure efficient monitoring and data collection. This paper focuses on enhancing strength in WSNs using a neural network algorithm. Efficient sensor node deployment is paramount for maximizing coverage in WSNs and presents a novel, data-driven approach for coverage optimization that clouts the power of neural networks. Our method employs a specifically designed neural network architecture to capture the complex interplay between sensor placement and the resulting monitored area. The network is trained and empowers it to predict optimal node locations that guarantee the most extensive coverage within the designated area. Compared to traditional methods, it facilitates highly precise coverage optimization and the backpropagation algorithm refines mobile sensor placement over time, potentially reducing coverage gaps by 40–45 percent, leading to a more optimized network. Finally, the architecture has the potential to be scaled for efficient deployment in large-scale WSNs.