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Accurate brain tumour segmentation in Multimodal Multi-parametric Magnetic Resonance Imaging is critical for clinical intervention but remains challenging due to complex boundaries and heterogeneous morphology. Existing U-Net architectures often struggle to capture global context and accurately delineate tumours with irregular or diffuse boundaries, limiting their clinical applicability. To mitigate these challenges, We introduce a novel dual-path 3D U-Net framework that integrates Optimal Transport (OT) theory into feature fusion, enabling improved alignment of global and local features. The architecture includes a Global Context Path (via Pyramid Pooling) and a Local Detail Path (with multi-scale convolutions), capturing complementary spatial features. The Optimal Transport Fusion module leverages Optimal Transport theory to efficiently align and merge features from both paths, offering a principled alternative to traditional fusion strategies such as concatenation or attention mechanisms. Additionally, we incorporate an edge-aware loss function based on 3D Sobel operators, which refines boundary precision in segmentation masks. Evaluated on the BraTS 2023 glioma dataset, our model outperforms standard U-Net and other recent state-of-the-art models, demonstrating the effectiveness of Optimal Transport-based feature fusion in enhancing brain tumour segmentation accuracy.