Since 2019, the Harris Hawks Optimization algorithm has become a robust metaheuristic for complex optimization, valued for fast convergence and adaptability. This review synthesizes advances in HHO research, covering theory, variants, and applications. It first outlines HHO’s core mechanism—dynamic switching between exploration and exploitation—to balance global search and local refinement. Four key enhancement strategies are analyzed: chaotic initialization for diversity, binary adaptations for discrete problems, multi-strategy integrations to avoid stagnation, and hybridizations with other algorithms. HHO’s impacts across six domains—neural network tuning, engineering design, feature selection, medical diagnostics, energy scheduling, and network security—are highlighted. While HHO offers high solution quality and robustness, challenges like premature convergence and parameter sensitivity remain. Proposed solutions include adaptive parameter control and gradient-informed strategies. Emerging frontiers, such as quantum-inspired variants and dynamic multi-objective adaptations, are outlined to guide future research. This survey consolidates HHO advancements and provides guidelines for tailoring the algorithm to evolving computational needs.