Spatial multi-omics integrates multiple omics layers to provide spatial context for cellular and molecular analysis, addressing limitations of single-cell sequencing. This review discusses advancements in spatial multi-omics technologies, including improvements in throughput, resolution, and integration of modalities. Spatial multi-omics enables precise localization of cells within tissues and quantification of gene expression in situ, offering insights into intracellular and intercellular molecular mechanisms involved in disease development, physiology, and pathogenesis. It also aids in constructing spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping. Spatial transcriptomics, genomics, proteomics, metabolomics, and epigenomics are key technologies in this field. Spatial transcriptomics has enhanced understanding of cellular organization and intra-tissue interactions by systematically measuring gene expression levels across tissue space. Techniques such as smFISH, MERFISH, seqFISH, ISS, and FISSEQ enable high-resolution detection of RNA molecules within tissues. Spatial genomics provides insights into the spatial organization of cell types and chromatin states, revealing genetic aberrations and environmental cues within tumors. Spatial proteomics involves the large-scale study of proteins, including their expression levels, post-translational modifications, and protein-protein interactions, providing a comprehensive understanding of disease occurrence and cell metabolism at the protein level. Spatial epigenomics examines modifications to DNA sequence and chromatin structure that regulate gene activity without altering the genetic code. Spatial metabolomics involves the initial detection and quantification of metabolites present in biological material, providing insights into cellular activities such as cell signaling, energy transfer, and intercellular communication. Integration of spatial multi-omics enables simultaneous analysis of multiple data modalities, such as transcriptomics, proteomics, genomics, epigenomics, and metabolomics, with the same tissue section. This integration provides a comprehensive view of biological systems, revealing associations and heterogeneities between transcriptomes and metabolomes across different tissue regions. Applications of spatial multi-omics include deciphering spatial-specific atlas production of molecular and cellular profiles, understanding spatial-based heterogeneity in human diseases, and identifying potential therapeutic targets. Spatial multi-omics has significant potential for advancing our understanding of complex biological systems and improving disease diagnosis and treatment.Spatial multi-omics integrates multiple omics layers to provide spatial context for cellular and molecular analysis, addressing limitations of single-cell sequencing. This review discusses advancements in spatial multi-omics technologies, including improvements in throughput, resolution, and integration of modalities. Spatial multi-omics enables precise localization of cells within tissues and quantification of gene expression in situ, offering insights into intracellular and intercellular molecular mechanisms involved in disease development, physiology, and pathogenesis. It also aids in constructing spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping. Spatial transcriptomics, genomics, proteomics, metabolomics, and epigenomics are key technologies in this field. Spatial transcriptomics has enhanced understanding of cellular organization and intra-tissue interactions by systematically measuring gene expression levels across tissue space. Techniques such as smFISH, MERFISH, seqFISH, ISS, and FISSEQ enable high-resolution detection of RNA molecules within tissues. Spatial genomics provides insights into the spatial organization of cell types and chromatin states, revealing genetic aberrations and environmental cues within tumors. Spatial proteomics involves the large-scale study of proteins, including their expression levels, post-translational modifications, and protein-protein interactions, providing a comprehensive understanding of disease occurrence and cell metabolism at the protein level. Spatial epigenomics examines modifications to DNA sequence and chromatin structure that regulate gene activity without altering the genetic code. Spatial metabolomics involves the initial detection and quantification of metabolites present in biological material, providing insights into cellular activities such as cell signaling, energy transfer, and intercellular communication. Integration of spatial multi-omics enables simultaneous analysis of multiple data modalities, such as transcriptomics, proteomics, genomics, epigenomics, and metabolomics, with the same tissue section. This integration provides a comprehensive view of biological systems, revealing associations and heterogeneities between transcriptomes and metabolomes across different tissue regions. Applications of spatial multi-omics include deciphering spatial-specific atlas production of molecular and cellular profiles, understanding spatial-based heterogeneity in human diseases, and identifying potential therapeutic targets. Spatial multi-omics has significant potential for advancing our understanding of complex biological systems and improving disease diagnosis and treatment.