Wireless sensor network (WSN) is a collection of specialized transducers linked by a communication system that monitors and records conditions in various locations. The process of determining an appropriate route from the destination node to the Source node. Due to the routing problem, networks last less time and use more energy. In this paper, a novel energy efficient cluster-based data redundancy elimination (EC-DRE) technique has been proposed for improving network lifetime and optimal routing in WSN. The k-means clustering algorithm is used in the proposed EC-DRE technique, and the Salp Swarm Optimization Algorithm is used to choose the Cluster Head (CH). The EC-DRE methodology uses the African vulture optimization method, which takes into account fitness criteria such as distance, network lifetime, energy, and delay when routing data. By using the normalised standard deviation methodology, the suggested method also reduces data redundancy, which was done in two steps, namely Intra Cluster and Inter Cluster. Prior to clustering, intra clustering was carried out. Data redundancy is filtered by Inter Cluster before the routing process. The presented EC-DRE has been simulated using MATLAB The proposed framework is compared with existing techniques such as E-ESR, FPTAC, DH, and PSO in terms of network life time, number of clusters, energy consumption, and residual. Experimental results shows that the suggested EC-DRE approach extends network lifetime by 48.37%, 42.53%, 33.11%, and 21.42% compared to E-ESR, FPTAC, DH, and PSO Methods.