Masked face segmentation plays a crucial role in face recognition, security, and medical applications, yet remains challenging due to occlusions, varying lighting conditions, and diverse mask patterns. To address these issues, we propose MBFANet, a lightweight and efficient deep learning model that integrates multi-branch feature fusion and attention mechanisms for real-time segmentation. Our architecture is built upon RepVGG, an efficient convolutional backbone, and is enhanced by two key modules: Channel Aggregation and Coordinate Refinement (CACR) for improving spatial and channel-wise feature representation, and the Atrous Pyramid Attention Module (APAM) for capturing multi-scale contextual information. Additionally, a Multi-Branch Feature Fusion (MBFF) module is employed in the decoder to refine segmentation outputs. To optimize segmentation performance, we introduce a hybrid loss function that combines Dice Loss and Focal Loss, effectively addressing class imbalance while improving boundary precision. Experimental evaluations on the Masked Face Segmentation Dataset (MFSD) demonstrate that MBFANet achieves superior segmentation accuracy, outperforming existing models in IoU, F1-score, recall, and precision while maintaining a low parameter count (10M) and high inference speed (153 FPS). Ablation studies confirm the effectiveness of the proposed modules, showing consistent improvements across all evaluation metrics. With its balance between accuracy, efficiency, and computational cost, MBFANet presents a highly effective solution for real-time masked face segmentation, making it well-suited for practical deployment in security, biometric authentication, and mobile applications.