<p>This paper provides a comprehensive analysis of the coati optimization algorithm (COA) and its applications across diverse academic domains. Introduced as a novel bio-inspired metaheuristic algorithm, COA is motivated by the natural behaviors of coatis, particularly their strategies for hunting iguanas and escaping from predators. These behaviors are mathematically modeled through two main phases corresponding to exploration and exploitation, enabling COA to address complex optimization problems effectively. By maintaining a well-balanced trade-off between global search and local refinement, COA demonstrates strong search efficiency and robustness. Since its introduction, COA has attracted growing attention from the research community, with numerous studies published in reputable international journals and conference proceedings, including outlets such as Springer, Elsevier, MDPI, Taylor &amp; Francis, IEEE, Oxford University Press, and Wiley. Existing COA-based studies encompass a wide range of applications, including standard benchmark functions and real-world engineering optimization problems, as well as various extensions such as improved, hybrid, and application-specific variants. This paper systematically reviews the available literature on COA, categorizing existing studies according to their methodological developments and application areas, and highlights emerging research trends and future research directions.</p>