The emergence of spatial multi-omics has addressed the limitations of single-cell sequencing by integrating the genome, transcriptome, proteome, metabolome, and epigenome to enhance understanding of cell biology and human diseases. This review examines advancements in multi-omics technologies, focusing on their evolution, improvements in throughput and resolution, modality integration, and accuracy. Spatial multi-omics reveals spatial heterogeneity, constructs detailed spatial atlases, and advances translational research and cancer therapy through precise spatial mapping. Key techniques include spatial transcriptomics, which enhances understanding of cellular organization and intra-tissue interactions, and spatial genomics, which quantifies genetic aberrations and environmental cues within tumors. Spatial proteomics provides large-scale protein expression and localization data, while spatial epigenomics examines DNA modifications and chromatin structure. Spatial metabolomics analyzes metabolite distribution and dynamics. The integration of these omics layers through spatial multi-omics enables simultaneous analysis of multiple data modalities, such as transcriptomics, proteomics, genomics, epigenomics, and metabolomics, within the same tissue section. This integration has significant applications in producing spatial-specific atlases, decoding spatial heterogeneity in human diseases, and advancing tumor immunology and cancer therapy. Spatial multi-omics has been used to construct molecular and cellular profiles in healthy and diseased states, revealing spatial heterogeneity in tumors, and identifying potential therapeutic targets. It also facilitates lineage tracking and spatial trajectory analysis, offering new research avenues in biological and biomedical fields.The emergence of spatial multi-omics has addressed the limitations of single-cell sequencing by integrating the genome, transcriptome, proteome, metabolome, and epigenome to enhance understanding of cell biology and human diseases. This review examines advancements in multi-omics technologies, focusing on their evolution, improvements in throughput and resolution, modality integration, and accuracy. Spatial multi-omics reveals spatial heterogeneity, constructs detailed spatial atlases, and advances translational research and cancer therapy through precise spatial mapping. Key techniques include spatial transcriptomics, which enhances understanding of cellular organization and intra-tissue interactions, and spatial genomics, which quantifies genetic aberrations and environmental cues within tumors. Spatial proteomics provides large-scale protein expression and localization data, while spatial epigenomics examines DNA modifications and chromatin structure. Spatial metabolomics analyzes metabolite distribution and dynamics. The integration of these omics layers through spatial multi-omics enables simultaneous analysis of multiple data modalities, such as transcriptomics, proteomics, genomics, epigenomics, and metabolomics, within the same tissue section. This integration has significant applications in producing spatial-specific atlases, decoding spatial heterogeneity in human diseases, and advancing tumor immunology and cancer therapy. Spatial multi-omics has been used to construct molecular and cellular profiles in healthy and diseased states, revealing spatial heterogeneity in tumors, and identifying potential therapeutic targets. It also facilitates lineage tracking and spatial trajectory analysis, offering new research avenues in biological and biomedical fields.