Multi-modal medical image fusion seeks to integrate complementary information from heterogeneous imaging modalities to enhance clinical diagnostics. However, it faces challenges in reconciling channel-wise feature conflicts, spatial misalignments, and redundant feature propagation. In this paper, we propose a Triple Expert Adaptation Network (TEA-Net) to address these limitations through three domain-specific fusion mechanisms: Channel Expert Adaptation Fusion (CEAF), Low-Rank Expert Adaptation Fusion (LoR-EAF), and Spatial Expert Adaptation Fusion (SEAF). The CEAF employs globally dynamic channel attention to resolve intensity conflicts across different modalities. The LoR-EAF focuses on modulating the most relevant interactions between modalities in low-rank to compress extraneous information. The SEAF integrates large-kernel convolutions with cross-modality interactions to align features across different modalities. To boost fusion, we introduce an adaptive prompt selection module that utilizes learnable prompts to guide fusion among experts. Experiments on publicly available datasets demonstrate the superiority of our TEA-Net over state-of-the-art methods. The source codes are available at https://github.com/supersupercong/TEA-Net .