<p>Early identification of brain tumors is crucial for cancer diagnosis since it can greatly increase survival chances. Brain tumors, which are defined as abnormal cell growth inside the brain, are among the worst types of cancer. Accurate diagnosis and effective treatment of brain cancers heavily rely on medical imaging. However, early detection is inherently challenging due to the elusive nature of tumors. To analyze malignancies, magnetic resonance imaging (MRI) pictures of patients are crucial. Due to several misclassifications, manual procedures have been unable to keep up with the volume of created data. With a deep transfer learning method, five primary working modules are used in the suggested research work to deal with huge amounts of data and reduce miss-classification first, the tumor pictures are stripped of their skulls, Afterward, the skull-stripped images are filtered using the guided bilateral filter (GBF). Subsequently, the areas affected by brain tumors are segmented using the OTSU optimum thresholding approach and transformed utilizing the wavelet-based reconstruction technique. The primary features of the MRI image are extracted using the improved Gabor wavelet Transform (IGWT) technique. The Grey Wolves Optimization (GWO) method selects the best features, which are then supplied to the deep-transfer-learning classification model. In this paper, we employed five deep transfer learning architectures: VGG19, InceptionV3, InceptionResNetV2, ResNet152, and DenseNet121. A dataset that was assembled from three benchmark databases used in our research. DenseNet121 deep transfer learning model works better than other techniques, as demonstrated by the testing results, which show an accuracy of 99.43%.</p>