Incidences of brain tumor has increased world- wide in the recent years. Magnetic Resonance Imaging (MRI) stands as a pivotal imaging modality in the med-ical domain, offering unparalleled accuracy in brain tumor diagnosis. The high-resolution images generated by MRI facilitate early tumor detection and play a crucial role in decision-making processes. The present study explores the utilization of deep transfer models such as VGG16, VGG19, INCEPTIONV3, RESNET50 and EFFICIENTB0 to detect and classify different types of brain tumors, in-cluding Glioma Tumor (GT), Meningioma Tumor (MT), Pituitary Tumor (PT), and cases with No Tumor (NT). The original images were preprocessed to overcome data imbalance. The preprocessed images were applied to vari-ous transfer learning models before and after tuning. The accuracy was higher in all the models after appropriate tuning and INCEPTIONV3 outperformed among all the other tuned deep transfer models with a testing accuracy of 95%. The developed deep transfer model will act as a decision support system and help the medical expert in efficient brain tumor diagnosis.