The brain tumor is among the most hazardous and destructive diseases. The mortality rate in brain cancer is more at a later stage. Also, the brain tumor's misdiagnosis will produce danger and reduce the patient's chances of survival. The early diagnosis of a brain tumor aids in saving the life of the affected person by providing the right treatment. The computer-aided medical imaging techniques like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) aids in diagnosing the disease. Hence brain MRI classification became an active research area in recent years. Numerous methods have been presented in the earlier period for MRI categorization, right from classical methods to advance Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN). The conventional Machine Learning (ML) techniques required handcrafted features while CNN performs classification by drawing features from unprocessed images directly via the convolution and pooling layer's parameter tuning. The feature extraction using the CNN algorithm is mostly influenced by the size of the training process's images. CNN models overfit after some epoch if the training dataset size is small. Therefore, transfer learning techniques have evolved. In the proposed system, conduct five studies using five transfer learning architectures such as AlexNet, Vgg16, ResNet18, ResNet50, and GoogLeNet to classify the clinical dataset of brain MRI into benign and malignant. Data augmentation techniques are applied over the brain MRI to generalize the results and reduce overfitting possibilities. In this proposed system, fine-tuned AlexNet architecture achieved the highest precision, recall, and f-measure value of 0.937, 1, and 0.96774, respectively