The review discusses the advancements and applications of mass spectrometry imaging (MSI) in spatially resolved multi-omics analysis, focusing on metabolomics, lipidomics, and proteomics. MSI is highlighted as a leading technique for mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples, offering high spatial resolution and sensitivity. The review covers various MSI techniques, including label-free and antibody-based methods, and their recent advancements such as enzymatic-assisted multimodal imaging, chemical derivatization, and ion mobility separation. These techniques enhance molecular coverage, sensitivity, and specificity, enabling the detection of low-abundance and large biomolecules. The integration of MSI with other imaging modalities and the use of artificial intelligence for data analysis are also discussed, emphasizing the potential of MSI in providing comprehensive insights into biological systems. The review concludes by outlining future directions and the need for more powerful data analysis tools to handle the increasing complexity of MSI datasets.The review discusses the advancements and applications of mass spectrometry imaging (MSI) in spatially resolved multi-omics analysis, focusing on metabolomics, lipidomics, and proteomics. MSI is highlighted as a leading technique for mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples, offering high spatial resolution and sensitivity. The review covers various MSI techniques, including label-free and antibody-based methods, and their recent advancements such as enzymatic-assisted multimodal imaging, chemical derivatization, and ion mobility separation. These techniques enhance molecular coverage, sensitivity, and specificity, enabling the detection of low-abundance and large biomolecules. The integration of MSI with other imaging modalities and the use of artificial intelligence for data analysis are also discussed, emphasizing the potential of MSI in providing comprehensive insights into biological systems. The review concludes by outlining future directions and the need for more powerful data analysis tools to handle the increasing complexity of MSI datasets.