The rapid development of spatial multi-omics technologies has enabled the simultaneous acquisition of transcriptomic, proteomic, and epigenomic information from the same tissue section. However, substantial differences in distributional properties, data dimensionality, and noise levels across modalities, together with the inherent sparsity and incompleteness of spatial information, pose major challenges for data integration and modeling. In recent years, deep learning-based spatial multi-omics integration algorithms have emerged rapidly, offering new approaches for constructing unified latent representations and achieving cross-modal fusion. In this review, we systematically summarize existing spatial multi-omics integration methods for the first time, categorizing and comparing them from two perspectives. We not only systematically surveyed the datasets employed by these methods, but also highlighted the key downstream analytical tasks they support, and further summarized the major challenges currently faced in spatial multi-omics integration research. Furthermore, we compare the strengths and limitations of different approaches to assist researchers in selecting appropriate methods more efficiently, thereby advancing the application of spatial multi-omics in uncovering multilayer regulatory mechanisms of tissue microenvironments and disease processes.
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