In recent years, the quest for optimizing metaheuristic algorithms has led to a surge in research efforts aimed at enhancing their performance. While existing reviews have diligently summarized these endeavors, they primarily focus on presenting the collective body of work undertaken to augment standard algorithms. In contrast, this paper takes a unique perspective by concentrating on the myriad methodologies employed by authors to improve one such algorithm, the Sine Cosine Algorithm (SCA). Our comprehensive review dissects the various strategies used to elevate the effectiveness of SCA variants, meticulously scrutinizing their advantages and disadvantages. This in-depth analysis extends beyond the confines of SCA and provides valuable insights into the broader landscape of metaheuristic optimization algorithms. By evaluating the pros and cons of these enhancement methods, our work forms a foundational review that can be applied to other optimization algorithms. Through this broader lens, we offer readers a comprehensive overview of the strategies adopted by researchers in recent years to enhance optimization algorithms, facilitating a deeper understanding of the advancement of this vital field. Our paper thus serves as a guidepost for researchers and practitioners navigating the ever-evolving terrain of metaheuristic optimization, shedding light on the strengths and potential pitfalls of enhancement methodologies. It provides a holistic perspective that empowers the community to make informed choices when selecting or devising strategies to optimize algorithms for diverse problem domains.