Optimization is essential in machine learning, significantly impacting model performance, training efficiency, and generalization. This paper provides a comprehensive review of optimization techniques, with a focus on with an emphasis on their applicability to deep learning and massive amounts of data. We discuss the classification of optimization methods, historical advancements, application challenges, and the latest innovations in adaptive algorithms, gradient-free methods, and domain-specific optimizations. Future research directions and recommendations for the field are proposed.