<p>The complexity of engineering optimization problems continues to grow, making classical gradient-based optimization methods increasingly inadequate due to their mathematical limitations. As a result, metaheuristic algorithms have gained popularity over exact methods because of their simplicity and robustness. Among these, population-based bio-inspired algorithms have demonstrated strong performance across a wide range of optimization problems. The Snake Optimizer (SO) is a bio-inspired metaheuristic algorithm that mimics the unique mating behavior of snakes. According to the literatures, SO outperforms many well-known metaheuristics on benchmark functions and real-world applications. Additionally, SO can be integrated with other artificial intelligence techniques, further enhancing its versatility. This study provides a comprehensive review of SO, covering its inspiration, variants, applications, and recent advancements. The systematic analysis aims to support the development of improved SO variants and hybrid approaches, guiding researchers toward the design of superior metaheuristic optimization algorithms with enhanced intelligent mechanisms. Furthermore, this review highlights the key challenges and potential research directions for future enhancements of SO, ensuring its continued relevance in solving complex engineering problems.</p>