Purpose: Schizophrenia (SZ) is a mental disorder that affects many young people. Early detection and treatment can release the stress of family members and save societal costs. Deep learning in neuro imaging creates new insights in modification of brain structures during various brain disorders. Therefore, resting state functional Magnetic Resonance Image(rsFMRI) data for schizophrenia is used in this paper. Method Initially, rsFMRI images in the dataset are preprocessed. After that, data augmentation is done and data is splitted into training and testing images. Then, the prediction model based on the deep learning framework RESNET 50 is constructed to extract features. Finally, the test images are given to the pre trained model to predict schizophrenia and Healthy Controls(HC). Result The proposed system produces the classification accuracy of 95.53% for predicting schizophrenia patients according to the experimental results. Conclusion Based on the comparative analysis, we conclude that our model outperforms some recent methods and also increases the schizophrenia prediction accuracy.