Medical image analysis is crucial for the accurate diagnosis and treatment of brain tumors in modern healthcare. This study introduces LiteNet, an efficient feature fusion model for brain tumor classification using MRI scans. Combining EfficientNetB0 and MobileNetV2, LiteNet integrates a Lightweight Feature Extraction Module (LEM) and a Feature Fusion Module (FFM) to enhance feature representation while maintaining computational efficiency. Evaluated on Figshare and Br35H datasets, LiteNet achieved accuracies of 99.02% and 99.83%, respectively. Precision, recall, and F1-Score of 99.89%, 98.88%, and 98.88% on Figshare, and consistent 99.83% on Br35H, indicate its robustness on diverse datasets. We utilized Grad-CAM visualization to provide interpretability by highlighting important regions contributing to classification decisions. These impressive results underscore LiteNet’s capability to accurately detect and classify brain tumors, making it a valuable tool for enhancing diagnostic accuracy in clinical settings, particularly in resource-limited environments.