Magnetic resonance imaging (MRI), with its high resolution and radiation-free characteristics, has become a crucial tool for brain tumor diagnosis. We classified brain tumors into non-tumors, glioma, meningioma, and pituitary tumors by integrating public image datasets with preprocessing and data augmentation techniques and employing four deep learning models, such as a convolutional neural network (CNN), visual geometry group network 19 (VGGNet 9), residual network 101 version 2 (ResNet101V2), and efficient network version 2 b2 (EfficientNetV2B2). VGGNet19 and CNNs excelled in accuracy and stability, while EfficientNetV2B2 was efficient yet required refinement for specific categories, and ResNet101V2 benefited from further optimization. Deep learning significantly enhances diagnostic efficiency and accuracy, assisting clinical decision-making and improving patient survival rates.