The development of new metaheuristic algorithms and their enhancements has seen significant growth, yet many of these algorithms share similar limitations. This is largely due to insufficient studies analyzing their structures and performance prior to proposing modifications. The Tunicate Swarm Algorithm (TSA), a recently developed nature-inspired algorithm, offers a simple structure, distinctive stabilizing features, and impressive efficiency. Inspired by the social behaviors of tunicates and their jet propulsion for movement and foraging, the TSA employs a dynamic weighting mechanism to simulate their influence during the search process. Its notable traits, including simplicity, adaptability, minimal parameters, and independence from derivatives, have contributed to its rapid adoption across various optimization problems. This review focuses on the foundational research underlying the TSA, exploring its development and effectiveness as highlighted in existing studies. It also examines enhancements to the algorithm's behavior, particularly efforts to align search space geometry with practical optimization challenges. Finally, potential directions for future improvements and adaptations are proposed to further advance the TSA's capabilities.