The departure from mono-exponential decay of the diffusion-induced signal loss has promoted the research of anomalous diffusion in MRI. It has been found that anomalous diffusion models offer substantial advantages over the conventional method in clinical applications. However, these models require more diffusion weightings for complicated estimation procedure, which prevents its further application. In this study, we demonstrated that machine learning can be applied to accelerate the estimation of anomalous diffusion parameters. Furthermore, feature selection was used to identify the most relevant signals, thus helping to reduce the extensive sets of diffusion weightings.