This review paper provides an in-depth exploration of optimization techniques in Operations Research (OR), highlighting their significance, recent advancements, challenges, and future directions. Operations Research utilizes mathematical and analytical methods to optimize decision-making processes across various industries. The paper begins with an overview of OR, discussing its definition, scope, and historical development. It then delves into the fundamentals of optimization, covering different types of optimization problems and algorithms. Classical optimization techniques such as Linear Programming, Integer Programming, and Nonlinear Programming are discussed, followed by an exploration of heuristic and metaheuristic optimization techniques including Genetic Algorithms, Simulated Annealing, Tabu Search, Particle Swarm Optimization, and Ant Colony Optimization. Recent advances in optimization, including hybrid methods, multi-objective optimization, optimization for big data, and integration with machine learning and artificial intelligence, are analyzed. Furthermore, the paper examines the challenges faced by optimization research and outlines emerging trends and future prospects. By synthesizing current knowledge and identifying areas for future research, this paper aims to contribute to the ongoing development and application of optimization techniques in OR and related fields.