This paper provides a comprehensive review of the Manta Ray Foraging Optimization (MRFO) algorithm, which was introduced in 2020. MRFO is a novel metaheuristic algorithm inspired by the foraging behaviors of manta rays, specifically the cyclone, chain, and somersault foraging techniques. These biological strategies enable effective solutions to complex physical challenges. The algorithm's strong exploitation and exploration capabilities make it a promising solution for complex optimization problems. Since its inception, MRFO has been widely studied and applied in various academic fields, with numerous research papers published in prestigious journals and conference proceedings. The paper highlights the growing interest in MRFO, noting that 12%, 31%, 8%, and 49% of studies focus on hybridization, improvement, other variants, and optimization challenges, respectively. The review aims to consolidate the available literature on MRFO applications, covering various adaptations and variants, as well as the challenges they face. The authors identify that MRFO does not maintain a balanced equilibrium between exploration and exploitation, suggesting that further research is needed to address this issue. The paper is structured as follows: Sect. 2 provides an overview of the motivation for MRFO and its mathematical model. Sect. 3 discusses all versions and modifications of MRFO, categorizing them into hybridization, improvement, variations, and optimization challenges. Sect. 4 delves into the specific applications and enhancements of MRFO. The review aims to provide a thorough understanding of MRFO and its potential in solving complex optimization problems across various multidisciplinary areas.This paper provides a comprehensive review of the Manta Ray Foraging Optimization (MRFO) algorithm, which was introduced in 2020. MRFO is a novel metaheuristic algorithm inspired by the foraging behaviors of manta rays, specifically the cyclone, chain, and somersault foraging techniques. These biological strategies enable effective solutions to complex physical challenges. The algorithm's strong exploitation and exploration capabilities make it a promising solution for complex optimization problems. Since its inception, MRFO has been widely studied and applied in various academic fields, with numerous research papers published in prestigious journals and conference proceedings. The paper highlights the growing interest in MRFO, noting that 12%, 31%, 8%, and 49% of studies focus on hybridization, improvement, other variants, and optimization challenges, respectively. The review aims to consolidate the available literature on MRFO applications, covering various adaptations and variants, as well as the challenges they face. The authors identify that MRFO does not maintain a balanced equilibrium between exploration and exploitation, suggesting that further research is needed to address this issue. The paper is structured as follows: Sect. 2 provides an overview of the motivation for MRFO and its mathematical model. Sect. 3 discusses all versions and modifications of MRFO, categorizing them into hybridization, improvement, variations, and optimization challenges. Sect. 4 delves into the specific applications and enhancements of MRFO. The review aims to provide a thorough understanding of MRFO and its potential in solving complex optimization problems across various multidisciplinary areas.