Hyperspectral image pansharpening aims to restore high-spatial-resolution hyperspectral data by integrating the fine spatial details of a panchromatic image with the rich spectral information from a low-resolution hyperspectral cube. Existing methods typically perform feature fusion either at low resolution or during upsampling, which often leads to incomplete multi-level feature integration. Moreover, the high dimensionality of hyperspectral data—with its numerous spectral bands—demands abundant feature channels, yet most prior works fail to fully exploit these channels, resulting in underutilized spectral information and suboptimal fusion. To address these challenges, we propose MFMamba, a novel network employing optimized multi-stage feature fusion. At low resolution, MFMamba extracts and fuses features across multiple depths; during reconstruction, it further integrates high-resolution features, ensuring thorough utilization of both spectral and spatial cues. At the core of our architecture is the HyperMamba Block, a tri-branch module designed to disentangle and refine spectral and spatial features. Additionally, we incorporate a channel attention mechanism within each block to uniformly enhance informative responses across all channels, mitigating incomplete channel activation. Extensive experiments on three benchmark hyperspectral datasets demonstrate that MFMamba outperforms state-of-the-art pansharpening methods in both quantitative metrics and visual fidelity.