Brain tumors, particularly aggressive variants, drastically diminish life expectancy. This study endeavors to enhance the early detection and classification of brain tumors, focusing on glioma, meningioma, and pituitary tumors through a comprehensive methodological approach. Utilizing advanced deep learning techniques, including Convolutional Neural Networks (CNNs) augmented by transfer learning, we employ pre-trained models such as the Visual Geometry Group’s 16-layer network (VGG-16) and the 50-layer Residual Network (ResNet50). Additionally, we integrate traditional machine learning methods like Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) to further refine our analysis. The CNN models, particularly when enhanced with transfer learning, demonstrate exceptional diagnostic accuracy. Specifically, the CNN model achieved a notable accuracy rate of 93.90% and an F1 score of 93.07%. This research highlights the significant potential of artificial intelligence in healthcare diagnostics, particularly in medical image analysis for brain tumor classification, offering profound implications for improving patient outcomes.