Recent research on congestion avoidance in wireless sensor networks (WSNs) focuses on developing intelligent and energy-efficient mechanisms to prevent data packet loss, reduce latency, and enhance network lifetime under high traffic conditions. Modern approaches employ clustering-based routing, adaptive rate control, and priority-aware queue management to balance traffic loads among sensor nodes. Machine learning and optimization algorithms such as reinforcement learning, ant colony optimization, and genetic algorithms are increasingly used to predict congestion and dynamically adjust data transmission rates. Trust-aware multipath routing and cross-layer optimization techniques are also being integrated to maintain network stability and security while minimizing congestion in dense and heterogeneous WSN environments.