Recent research on load balancing in wireless sensor networks (WSNs) emphasizes optimizing energy consumption, prolonging network lifetime, and improving data transmission efficiency through intelligent task and traffic distribution among sensor nodes. Advanced techniques such as clustering-based routing, fuzzy logic, and swarm intelligence algorithms like ant colony optimization, particle swarm optimization, and elephant herding optimization are widely used to achieve balanced energy utilization. Machine learning and software-defined networking (SDN) approaches are also being integrated to dynamically adapt routing paths based on node energy and traffic density. These methods collectively enhance fault tolerance, minimize node congestion, and ensure stable network performance even in large-scale and heterogeneous WSN deployments.