Abstract: Accurate classification of brain tumors from magnetic resonance imaging (MRI) remains challenging due to complex spatial heterogeneity and limited annotated datasets. This study proposes a Multi-Staged Transfer Learning framework with progressive fine-tuning for brain disease classification using 3D-rendered MRI images. Rather than relying on slice-based inputs or computationally intensive volumetric convolutional networks, the proposed approach uses structured 3D-rendered representations to preserve spatial tumor morphology while maintaining compatibility with efficient 2D-pretrained architectures. A dual-backbone model integrating EfficientNet-B4 and MobileNetV2 was employed to extract complementary semantic and spatial features. Progressive fine-tuning was implemented across three stages, enabling controlled domain adaptation and improved optimization stability. The framework was evaluated on 4,244 3D-rendered MRI images categorized into glioma, meningioma, and tumor classes, using stratified data splits. Stage-wise optimization demonstrated progressive improvement, with the final stage achieving a peak validation accuracy of 94.51%, compared to 93.41% in Stage 0 and 91.21% in Stage 1. Class-wise evaluation showed strong discriminative performance, with high precision, recall, and F1-scores across all categories. The results indicate that structured staged adaptation combined with dual-backbone feature fusion enhances generalization performance while maintaining computational efficiency, providing a scalable solution for automated brain disease classification in clinical applications.