Cuckoo Search (CS) is a metaheuristic optimization algorithm developed by Xin-She Yang and Suash Deb. It is inspired by the brood parasitism behavior of cuckoo birds and the Lévy flight mechanism observed in nature. The algorithm uses a combination of random walks and Lévy flights to explore the search space efficiently. CS has been tested on various standard and stochastic test functions, demonstrating its effectiveness in finding global optima. It outperforms traditional algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) in terms of convergence speed and solution quality. The algorithm is also applied to engineering design problems, such as spring and welded beam design, where it achieves optimal solutions comparable or better than existing methods. CS is characterized by its ability to balance exploration and exploitation, making it suitable for complex, multi-modal optimization problems. The algorithm requires minimal parameter tuning, which enhances its robustness and applicability. The study highlights the potential of CS in solving a wide range of optimization problems, including multi-objective and NP-hard problems. Future research directions include further exploration of parameter sensitivity, hybridization with other algorithms, and mathematical analysis of the algorithm's structure.Cuckoo Search (CS) is a metaheuristic optimization algorithm developed by Xin-She Yang and Suash Deb. It is inspired by the brood parasitism behavior of cuckoo birds and the Lévy flight mechanism observed in nature. The algorithm uses a combination of random walks and Lévy flights to explore the search space efficiently. CS has been tested on various standard and stochastic test functions, demonstrating its effectiveness in finding global optima. It outperforms traditional algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) in terms of convergence speed and solution quality. The algorithm is also applied to engineering design problems, such as spring and welded beam design, where it achieves optimal solutions comparable or better than existing methods. CS is characterized by its ability to balance exploration and exploitation, making it suitable for complex, multi-modal optimization problems. The algorithm requires minimal parameter tuning, which enhances its robustness and applicability. The study highlights the potential of CS in solving a wide range of optimization problems, including multi-objective and NP-hard problems. Future research directions include further exploration of parameter sensitivity, hybridization with other algorithms, and mathematical analysis of the algorithm's structure.