<p>Automatic clustering into an optimal number of clusters poses a significant challenge. Various metaheuristic-based methods, such as the gravitational search algorithm, differential evolution, firefly algorithm, and particle swarm optimization, have been utilized in the literature to address this challenge. However, these methods suffer from poor solution precision due to the complexity of data, which limits their ability to achieve optimal clustering. To overcome this limitation, a novel approach called spiky gravitational search algorithm-based clustering is introduced. The proposed method employs a new variant of the gravitational search algorithm, referred to as the spiky gravitational search algorithm, to generate optimal clusters. The effectiveness of the proposed method is evaluated on two sets of benchmark functions, namely CEC-2015 and CEC-2019. Furthermore, its clustering performance is tested against six existing clustering methods on five UCI datasets, using three clustering metrics namely, adjusted rand index, normalized mutual information, and clustering accuracy. The proposed method outperforms existing techniques, achieving clustering accuracies of 96.3%, 94.2%, 91.6%, 97.5%, and 94.8% on the five UCI datasets, respectively. In addition, its segmentation performance is assessed on seven images both qualitatively and quantitatively. The proposed method surpasses the considered approaches, reporting the highest average values for Dice coefficient (0.803), structural similarity index measure (0.735), and feature similarity index measure (0.811). Experimental results highlight the robustness, efficiency, and superior performance of the proposed method.</p>