Optimization techniques are fundamental to machine learning, driving the process of adjusting model parameters to minimize error and improve predictive performance. These techniques help in efficiently training models by finding the best parameters that optimize a given objective function, often involving large-scale data and complex models. Recent innovations focus on adaptive and gradient-based methods that accelerate convergence and enhance robustness across diverse applications.