To provide a reliable and efficient service, load balancing plays an important role in wireless sensor networks (WSNs). There is a need to maximize the network lifetime for WSNs applications with periodic generation of data. Due to the relationship between energy consumption and network sensor node lifetime, energy consumption in a network should be minimized and balanced in order to increase network lifetime. Energy-efficient load-balancing techniques are needed to solve this problem. In this paper, a particle swarm optimization (PSO)-based energy-efficient load-balancing technique is proposed, in which the required number of routing paths and energy consumption of different nodes and paths are calculated. Based on maximum residual energy, paths are selected and further PSO-based load balancing is performed among all the paths for data transfer at a particular point of time. The performance of the proposed technique is evaluated using real testbed and experimental results and shows that the proposed technique performs better than existing techniques in terms of network lifetime, energy consumption, throughput, number of alive nodes, number of data packets received, execution time, and convergence rate.