Machine learning has become a prevalent and powerful tool in many scientific and engineering disciplines. This last chapter presents a few methods that combine stochastic models with machine learning to advance the understanding and forecast of many complex dynamical systems. Machine learning can serve as the surrogate forecast model in data assimilation to improve the efficiency and accuracy of the ensemble forecast. Reciprocally, data assimilation helps to enhance the quality of training data and provides additional sample trajectories to train a machine learning model. In addition, machine learning facilitates the development of stochastic closures and parameterizations. It is also shown in this chapter that machine learning can help build stochastic neural differential equations that facilitate solving both the forward and the inverse problems.