Bipolar disorder is a mental ailment caused by maximal mood swings with emotional highs and lows. Nowadays, this has become the most common abnormality related to mental health and furthermore it is ignored by people of all age groups. Bipolar disease is generally heritable but not all siblings of the family will be having it though, and will be having same genetics and the factors which can be risky. Here we use random forest algorithm, along with the Mag- netic Resonance Imaging (MRI) information. The utility of these irregularities in recognizing individual bipolar disorder patients from state of mind issue or health controls define patients dependent on their illness. Here we use machine learning algorithms like Random forest algorithm and CNN-mdrp(multimodal disease risk prediction) for the accuracy .We give the risk factor and stage of the healthy patient with the attributes we collected from the MRI. We use a trained dataset and machine learning algorithms mentioned above to get the output. Voxel-Based Morphometry (VBM) will be used to dividing and pre-processing the MRI infor- mation obtained. To see the changes in Gray Matter (GM) and White Matter (WM) of the different data groups individually, a simple equation is use and also the Principle Component Analysis will be used and The project gives you the output showing that CNN MDRP with random forest has high accuracy than other algorithms in bipolar disease prediction. © 2021 CEUR-WS. All rights reserved.