The early detection of brain tumors is crucial due to their highly dangerous nature and the potential for life-threatening consequences if left undiagnosed. Brain tumors significantly shorten life expectancy and cause extensive damage. To accurately diagnose brain tumors, medical imaging techniques such as MRI and other diagnostic tests play a vital role in the classification process. Artificial intelligence, specifically deep learning and computer vision, offers valuable techniques for detecting and classifying brain tumors. In this research, our focus is on developing an improved methodology for brain tumor classification. We implemented a proposed model using five pre-trained models: CNN, ResNet101, InceptionV3, VGG16, and VGG19. By employing data augmentation techniques, we enhanced their performance. The achieved Precision, Recall, and F1-Measure for unseen images were 95%, 95%, 95%, 97%, and 95%, respectively, as tested using three open datasets. Furthermore, aside from improving early tumor detection, these accuracy improvements have the potential to reduce disabilities such as paralysis. Data augmentation, accomplished through image rotation, scaling, and flipping, proves to be an effective means of enhancing model performance by generating new images with improved quality