In many applications, the Wireless Sensor Network (WSN) is utilized which needs network lifetime maximization. Several clusteringmethods have been developed, however they experience the adverse effects of irregular clusters, which ultimately make the network inconsistent. This triggers the energy hole problem around the base station. It is, therefore, the basis for a proper clustering of sensor mode, to guide information and effectively conserve energy, and to avoid accidental networkfailure due to the power drain. In this paper, proposed adaptive rain optimization (AROA) algorithm based on energy-efficient load balancing on WSN for efficient routing is proposed. To achieve this concept, two novel fitness functions are developed for the routing and clustering process. The proposed approach consists of two main stages such as clustering and routing. Initially, the sensor nodes are clustered to avoid the load balancing problem. After the clustering process, the routing is performed. The routing process improves the lifetime and decrease energy consumption in the network. The presentation of the projected approach is analyzed in terms of delay, energy consumption, throughput, drop, network lifetime, and overhead and delivery ratio. The projected technique is implemented with the consideration of NS2 simulator and presentation are contrasted with conventional techniques i.e.,Genetic Algorithm (GA)and Particle Swarm Optimization (PSO).