Recent research in congestion control and avoidance in wireless sensor networks emphasizes adaptive and intelligent strategies to manage traffic overload and ensure reliable data transmission. Modern techniques employ machine learning, reinforcement learning, and optimization algorithms to dynamically regulate data flow, adjust transmission rates, and reroute traffic based on network conditions. These methods focus on reducing packet loss, delay, and energy consumption while improving throughput and overall network performance. By integrating congestion-aware routing, buffer management, and priority-based scheduling, recent studies aim to enhance scalability and maintain efficient communication in dense and high-traffic WSN environments.