In this article, the Whale Optimization Algorithm (WOA) and several extended algorithms based on it are examined. The Whale Optimization Algorithm, as a novel metaheuristic approach, has gained a prominent position in solving complex optimization problems. This algorithm, inspired by the social behavior of whales in nature, has drawn attention due to its significant capabilities in finding global and local optima. However, WOA faces challenges such as a slow convergence rate, which can lead to increased computational time in solving problems, as well as a tendency to get stuck in local solutions, a limitation linked to the random selection of search agents during the exploration phase. These limitations have prompted researchers to enhance and improve the algorithm by integrating it with various methods and algorithms. In recent years, the combination of WOA with fuzzy and non-fuzzy methods has resulted in significant improvements in its performance, especially in problems like feature selection, data clustering, and complex engineering problems. These combinations have managed to maintain the inherent advantages of WOA while reducing its weaknesses, providing greater efficiency and accuracy in solving various problems. This article reviews several improved versions of the Whale Optimization Algorithm, which have been developed using different approaches. These algorithms are compared in terms of multiple aspects such as convergence rate, accuracy, complexity, average fitness value, avoidance of local optima, and their application to practical problems. Furthermore, the strengths and weaknesses of each of these versions are analyzed, and their applications in various scientific and industrial fields are discussed. The results of this study can serve as a comprehensive reference for researchers and engineers seeking to apply or develop advanced optimization algorithms. Additionally, this article contributes to a deeper understanding of the performance of the WOA and its enhancement methods and can be used as a foundation for future research in this field.