Pan-Mamba is a novel pan-sharpening network that leverages the efficiency of the Mamba model in global information modeling. The network includes two core components: channel swapping Mamba and cross-modal Mamba, designed to facilitate efficient cross-modal information exchange and fusion. Channel swapping Mamba initiates lightweight cross-modal interaction by exchanging partial panchromatic and multi-spectral channels, while cross-modal Mamba enhances information representation by exploiting inherent cross-modal relationships. Extensive experiments on diverse datasets demonstrate that Pan-Mamba outperforms state-of-the-art methods, showcasing superior fusion results in pan-sharpening. This work is the first to explore the potential of the Mamba model in pan-sharpening, establishing a new frontier in the field. The source code is available at <https://github.com/alexhe101/Pan-Mamba>.Pan-Mamba is a novel pan-sharpening network that leverages the efficiency of the Mamba model in global information modeling. The network includes two core components: channel swapping Mamba and cross-modal Mamba, designed to facilitate efficient cross-modal information exchange and fusion. Channel swapping Mamba initiates lightweight cross-modal interaction by exchanging partial panchromatic and multi-spectral channels, while cross-modal Mamba enhances information representation by exploiting inherent cross-modal relationships. Extensive experiments on diverse datasets demonstrate that Pan-Mamba outperforms state-of-the-art methods, showcasing superior fusion results in pan-sharpening. This work is the first to explore the potential of the Mamba model in pan-sharpening, establishing a new frontier in the field. The source code is available at <https://github.com/alexhe101/Pan-Mamba>.