Heuristic algorithms have evolved rapidly, leading to numerous theoretical breakthroughs and wide applications in various fields. Swarm intelligence algorithms, as an important branch of heuristic algorithms, have attracted significant attention from researchers. With the expansion of application scenarios and increasing problem complexity, swarm intelligence algorithms have faced considerable challenges in dealing with high-dimensional, multimodal complex function optimization. As a novel swarm intelligence algorithm, the Wolf Pack Algorithm (WPA) has emerged as an efficient tool for addressing such issues due to its unique swarm characteristics and the mechanism that simulates wolf pack hunting behavior. A systematic review and summary of WPA from the perspectives of biological characteristics and algorithm principles are of paramount significance. Subsequently, a comparison between WPA and commonly used swarm intelligence algorithms is conducted. Then, the research progress of WPA is discussed in terms of both theory and application. Finally, inadequacies of WPA are explored, and the future prospects are presented.