This paper provides a comprehensive survey of the Manta Ray Foraging Optimization (MRFO) algorithm, introduced in 2020, which is inspired by the foraging behaviors of manta rays—specifically cyclone, chain, and somersault foraging. MRFO is a novel metaheuristic algorithm that effectively addresses complex optimization problems due to its strong exploration and exploitation capabilities. It has been widely applied in various academic fields and has generated significant research interest, with numerous studies published in prestigious journals and conferences. The paper reviews the current state of MRFO research, categorizing studies into four main areas: hybridization (12%), improvement (31%), variations (8%), and optimization challenges (49%). Metaheuristics are nature-inspired algorithms that use randomness to explore solutions, making them effective for complex optimization problems. Unlike traditional methods, which often rely on gradient information and strict conditions, metaheuristics are more flexible and robust. Population-based metaheuristics, such as Genetic Algorithm (GA) and Differential Evolution (DE), are widely used for solving complex optimization problems. However, these methods may struggle with non-continuous, large-scale, and non-differentiable problems. Nature-inspired algorithms, such as the Artificial Gorilla Troops Optimizer (AGTO), Starling Murmuration Optimizer (SMO), and African Vultures Optimization Algorithm (AVOA), have shown superior performance in such scenarios. MRFO was developed based on the intelligent foraging behaviors of manta rays, which include three distinct movements: chain, cyclone, and somersault. These behaviors are used to find food efficiently, and MRFO mimics these to achieve a globally optimal solution. However, MRFO has been found to lack a balance between exploration and exploitation, requiring further improvements. This paper reviews the current state of MRFO, highlights its variations, and discusses its applications, benefits, and challenges. It also proposes future research directions, including hybridization, improvement, and application in multi-objective and binary optimization problems.This paper provides a comprehensive survey of the Manta Ray Foraging Optimization (MRFO) algorithm, introduced in 2020, which is inspired by the foraging behaviors of manta rays—specifically cyclone, chain, and somersault foraging. MRFO is a novel metaheuristic algorithm that effectively addresses complex optimization problems due to its strong exploration and exploitation capabilities. It has been widely applied in various academic fields and has generated significant research interest, with numerous studies published in prestigious journals and conferences. The paper reviews the current state of MRFO research, categorizing studies into four main areas: hybridization (12%), improvement (31%), variations (8%), and optimization challenges (49%). Metaheuristics are nature-inspired algorithms that use randomness to explore solutions, making them effective for complex optimization problems. Unlike traditional methods, which often rely on gradient information and strict conditions, metaheuristics are more flexible and robust. Population-based metaheuristics, such as Genetic Algorithm (GA) and Differential Evolution (DE), are widely used for solving complex optimization problems. However, these methods may struggle with non-continuous, large-scale, and non-differentiable problems. Nature-inspired algorithms, such as the Artificial Gorilla Troops Optimizer (AGTO), Starling Murmuration Optimizer (SMO), and African Vultures Optimization Algorithm (AVOA), have shown superior performance in such scenarios. MRFO was developed based on the intelligent foraging behaviors of manta rays, which include three distinct movements: chain, cyclone, and somersault. These behaviors are used to find food efficiently, and MRFO mimics these to achieve a globally optimal solution. However, MRFO has been found to lack a balance between exploration and exploitation, requiring further improvements. This paper reviews the current state of MRFO, highlights its variations, and discusses its applications, benefits, and challenges. It also proposes future research directions, including hybridization, improvement, and application in multi-objective and binary optimization problems.