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Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global and local searches during optimization, the BES algorithm effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers a comprehensive review of recent research on BES. Beginning with an introduction to BES's natural inspiration and conceptual optimization framework, it explores modifications, hybridizations, and applications of BES across various domains. Then, a critical evaluation of BES's performance is provided, offering an update on its effectiveness compared to recently published algorithms. Furthermore, the paper presents a meta-analysis of BES developments and outlines potential future research directions. As swarm-inspired metaheuristic algorithms become increasingly important in tackling complex optimization problems, this study is a valuable resource for researchers aiming to understand swarm-based algorithms, mainly focusing on BES comprehensively. It investigates BES's evolution, exploring its potential applications in solving intricate optimization challenges across diverse fields.