Recent research in deployment strategies for wireless sensor networks focuses on optimizing sensor placement to achieve maximum coverage, connectivity, and energy efficiency while minimizing deployment cost. Modern studies employ heuristic, meta-heuristic, and machine learning techniques such as Genetic Algorithms, Particle Swarm Optimization, and reinforcement learning to design adaptive and dynamic deployment models. These approaches aim to address challenges like uneven energy consumption, network partitioning, and environmental constraints, ensuring robust sensing performance and prolonged network lifetime. The integration of mobile sensors and intelligent repositioning further enhances flexibility and resilience in large-scale and heterogeneous WSN environments.