Abstract: The development of multi-modal magnetic resonance imaging (MRI) provides a new method for early diagnosis of brain diseases. Currently, diagnosis of most neuropsychiatric diseases is only based on clinical symptoms, which lacks objective neuroimaging biomarkers. As univariate analysis that is used in traditional research can only reveal disease-related structural and functional alterations at group level, which limits the clinical application. Recent attention has turned toward integrating multi-modal neuroimaging and machine learning to assist clinical disease diagnosis. Machine learning can obtain rules via automatically analyzing neuro-imaging data, and apply these rules to predict unknown data, to identify the brain regions highly correlated to the brain disease, to provide individual levels of diagnosis, and to detect pathophysiological mechanisms. This paper reviews the concrete steps of neuroimaging analysis based on machine learning and the application of machine learning in intelligent diagnosis and prediction of neuropsychiatric diseases. Finally, some new directions for future research are forecasted.