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In recent years, the field of data analytics has witnessed a surge in innovative techniques to handle the ever-increasing volume and complexity of data. Among these, nature-inspired algorithms have gained significant attention due to their ability to efficiently mimic natural processes and solve intricate problems. One such algorithm, the symbiotic organisms search (SOS) Algorithm, has emerged as a promising approach for clustering and predictive analytics tasks, drawing inspiration from the symbiotic relationships observed in biological ecosystems. Metaheuristics such as the SOS have been frequently employed in clustering to discover suitable solutions for complicated issues. Despite the numerous research works on clustering and SOS-based predictive techniques, there have been minimal secondary investigations in the field. The aim of this study is to fill this gap by performing a systematic literature …