Wireless Body Sensor Networks (WBSN) are distinguished through the large number of battery-powered WSNs. The most demanding feature of WBSN is the sensor node energy utilization, security as well as delay while managing the systematic abilities of the WSN. The number of clusters required to be fixed prior, and an efficient Cluster Head (CH) are challenging for identification, which will beyond minimize the entire performance of the network. To solve this issue, this research proposed the clustering approach using Exponential-Weighted Ant Lion Optimization (EWALO) for efficient cluster selection. To minimize the outliers from primary CH, the EWALO is proposed to identify the best CH, in which a fitness function is fixed based on the residual energy as well as the distance to the Base Station (BS), and a Chaotic map is utilized to speed up the EWALO convergence. The performance of the proposed ALO attained better results and it enhances the energy consumption of 0.16, throughput of 5.01 and delay of 1.7 respectively when compared to the existing methods like Self-Executing-Dynamic Cross-Propagation Clustering (SE-DCPC) and Hybrid Particle Swarm Optimization was combined with Improved Low-Energy Adaptive Clustering Hierarchy (HPSO-ILEACH) respectively.