The prognosis of brain tumor diseases is essential for effective treatment planning and patient management. This study investigates the use of Dense EfficientNet models, specifically an enhanced EfficientNet-B1, for the prognostication of multiclass brain tumor diseases. A dataset comprising 6462 MR images, including T1-W, T2-W, and FLAIR sequences, was classified into four categories: glioma, meningioma, no tumor, and pituitary tumors. The proposed method incorporates advanced data augmentation techniques, image cropping, and pixel resizing to improve training accuracy. Additionally, modifications to the EfficientNet architecture layers and the application of normalization and histogram equalization further enhance model performance.The results indicate that the enhanced EfficientNet-B1 model achieves a superior training accuracy of 98%, outperforming the EfficientNet-B0 model, with the highest accuracy observed in glioma tumor classification. Compared with other CNN architectures, such as ResNet50 and VGG-16, the EfficientNet-B1 model demonstrates higher performance and computational efficiency with fewer parameters.The study concludes that the enhanced EfficientNet-B1 model offers a robust and efficient solution for brain tumor detection and prognostication using MR images. Its innovative modifications and advanced preprocessing techniques significantly contribute to its high performance, making it a valuable tool for developing clinically useful applications for MR image analysis in brain tumor management.