In many fields, including engineering, medicine, and finance, optimization algorithms are essential for resolving challenging issues. These methods improve the efficacy of addressing real-world challenges, resource management, and decision-making. This paper provides a comprehensive review of optimization techniques in artificial intelligence (AI), exploring their algorithms, applications, and challenges. It includes classic methods like Gradient Descent and Simulated Annealing, population-based methods like Genetic and Evolutionary Algorithms, and specialized techniques like Particle Swarm Optimization and Ant Colony Optimization. This paper sheds light on the role of optimization in progressing AI and its applications by emphasizing its strengths, limitations, and social implications.