Abstract: Alzheimer's disease is a neurodegenerative disease occurring in middle-aged and elderly people, characterized by memory impairment and cognitive decline. At present, most of the multimodal imaging studies on Alzheimer's disease mainly focus on local specific brain regions, and there is a lack of in-depth exploration of whole brain network patterns. This paper summarizes the research on multimodal imaging of Alzheimer's disease based on machine learning and brain network. First, it introduces the definition of Alzheimer's disease and the limitations of machine learning technology in brain disease imaging research. Secondly, the general process of machine learning in brain network prediction is described, including: feature extraction, feature selection and feature dimensionality reduction, model construction and model evaluation; Finally, the research results of machine learning in Alzheimer's disease brain network of gray matter structure, white matter structure, resting-state functional brain network and multimodal fusion brain network are introduced in turn. By combing the research results in recent years, this paper has made the following three prospects for the future development direction of this field: large sample multi- center research, interpretable deep learning technology, and the establishment of longitudinal prediction models.