Brain imaging genomics is a burgeoning domain within data science， where an integrated analytical approach is applied to brain imaging and genomics data， frequently in conjunction with other biomarker， clinical， and environmental datasets. This strategy is employed to glean fresh insights into the phenotypic， genetic， and molecular features of the brain， along with their effects on both typical and atypical brain function and behavior. In light of the escalating significance of machine learning in biomedicine and the swiftly expanding corpus of literature in brain imaging genomics， this paper presents a current and exhaustive review of machine learning methodologies tailored for brain imaging genomics. Firstly， the related background and fundamental work in imaging genomics are reviewed. Then， we summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning and present methods for joint association analysis and outcome prediction. Finally， this paper discusses some prospects for future work.