Depression is one of the most complex psychiatric diseases that cause the most serious harm in today"s society. Searching for objective biomarkers of depression has always been the focus and difficulty of psychiatric research and clinical practice. Numerous studies have shown that magnetic resonance imaging (MRI) combined with artificial intelligence technology might be currently the most likely biologic marker to find breakthroughly in mental illness such as depression. However, the current potential objective biomarkers of depression based on psychiatric imaging have not been consistently concluded. From the perspective of combining psychoradiology with artificial intelligence technology represented by machine learning (ML) and deep learning (DL), this paper summarizes and analyses the related studies on depression from three components of the clinical practice including disease diagnosis, prevention and treatment for the first time. We found that a.the brain areas with diagnostic value are mainly concentrated in: precuneus, cingulate gyrus, inferior parietal lobule, insula , thalamus and hippocampus; b.the brain regions with preventive value are mainly concentrated in: precuneus, central posterior gyrus, dorsolateral prefrontal cortex, orbitofrontal cortex, middle temporal gyri; c.brain regions with predictive therapeutic response are mainly concentrated in: precuneus, cingulate gyrus, inferior parietal lobule, middle frontal gyrus, middle occipital gyrus, lingual gyrus. Future research can be improved by enlarging the sample size through multi-center collaboration and data transformation, and at the same time non-imaging data can be applied to data mining, which will help to improve the classification accuracy of artificial intelligence models, and provide scientific evidence and reference for the studies on exploring psychoradiological objective biomarkers for depression and its clinical application.