Momentum optimizers are advanced techniques used in gradient-based optimization to enhance the convergence speed of machine learning models. By incorporating a momentum term, these optimizers help accelerate the learning process in directions with low curvature while dampening oscillations in high curvature areas. This results in smoother and faster convergence towards the optimal solution. Commonly set to a default value of 0.9, momentum can be fine-tuned for better performance. Various strategies, such as Nesterov accelerated gradient and momentum decay, have emerged to further improve optimization efficiency, making momentum optimizers a vital component in deep learning applications.