The information extraction and analysis processes are blended with different data mining techniques like clustering, classifications, etc. Clustering is an explorative technique that extracts imperative information from large databases. Numerous metaheuristic algorithms have been reported for clustering and enhanced to handle dissimilar clustering problems like initialization, local optima, diversity, and convergence rate. Metaheuristic algorithms encompass multiple heuristic features and specific traits like nearer to premiere solution and computationally less significant. This research empowers the whale optimization algorithm (WOA) with two additional operational capabilities for solving clustering problems. The improved whale optimization algorithm (IWOA) integrates a chaotic map and neighborhood search strategy based on the “step division method”. The efficiency of IWOA is examined across six benchmark datasets and compared against six existing clustering algorithms. The experimental results validate the improvements made to the algorithm, affirming the effectiveness of the developed clustering approach.