Mass spectrometry imaging (MSI) is a powerful technique for spatially resolving multi-omics molecular mapping, enabling the visualization of metabolites, lipids, and proteins within tissue and cell samples. This review discusses the advancements and applications of MSI in spatially resolved metabolomics, lipidomics, and proteomics, highlighting the integration of MSI with other imaging modalities and the development of new methodologies to enhance molecular sensitivity and specificity. The review also explores the challenges and potential solutions in MSI applications, as well as the future trajectory of this technology. MSI techniques such as MALDI, SIMS, and DESI provide high spatial resolution and sensitivity for the analysis of biomolecules. Recent advancements in ion optics and ionization strategies have significantly improved the spatial resolution of MSI, allowing for resolutions down to the nanometer level. Additionally, the integration of ion mobility (IM) with MSI has enabled the separation of isomeric compounds within tissue samples. The development of high-spatial resolution 3D renderings of biological samples is also a promising frontier for MSI. Label-free MSI and antibody-based MSI are two main approaches for MSI. Label-free MSI allows the imaging of thousands of molecules without prior knowledge or the need for labels or antibodies. Antibody-based MSI, on the other hand, utilizes antigen-specific antibodies to recognize target analytes, providing high specificity and sensitivity for large biomolecules. The use of multiplexed antibody-based MSI has enabled the simultaneous detection of multiple proteins with high resolution. Recent advancements in MSI include the development of enzymatic-assisted multimodal MS imaging, which enhances the detection of large biomolecules and improves the sensitivity of low-abundance analytes. Chemical derivatizations have also been employed to enhance ionization efficiency and eliminate endogenous interferences, improving the visualization of specific molecules. The integration of artificial intelligence (AI) methods with MSI data analysis has also been explored, offering more comprehensive insights from biological samples. AI methods such as deep learning and neural networks have been used to analyze MSI data, improving the accuracy and efficiency of data analysis. Overall, MSI is a powerful tool for spatially resolved multi-omics molecular mapping, providing valuable insights into the molecular intricacies of living systems. The continued advancements in MSI technology are expected to further enhance the capabilities of this technique, enabling more comprehensive and accurate analysis of biomolecules in biological samples.Mass spectrometry imaging (MSI) is a powerful technique for spatially resolving multi-omics molecular mapping, enabling the visualization of metabolites, lipids, and proteins within tissue and cell samples. This review discusses the advancements and applications of MSI in spatially resolved metabolomics, lipidomics, and proteomics, highlighting the integration of MSI with other imaging modalities and the development of new methodologies to enhance molecular sensitivity and specificity. The review also explores the challenges and potential solutions in MSI applications, as well as the future trajectory of this technology. MSI techniques such as MALDI, SIMS, and DESI provide high spatial resolution and sensitivity for the analysis of biomolecules. Recent advancements in ion optics and ionization strategies have significantly improved the spatial resolution of MSI, allowing for resolutions down to the nanometer level. Additionally, the integration of ion mobility (IM) with MSI has enabled the separation of isomeric compounds within tissue samples. The development of high-spatial resolution 3D renderings of biological samples is also a promising frontier for MSI. Label-free MSI and antibody-based MSI are two main approaches for MSI. Label-free MSI allows the imaging of thousands of molecules without prior knowledge or the need for labels or antibodies. Antibody-based MSI, on the other hand, utilizes antigen-specific antibodies to recognize target analytes, providing high specificity and sensitivity for large biomolecules. The use of multiplexed antibody-based MSI has enabled the simultaneous detection of multiple proteins with high resolution. Recent advancements in MSI include the development of enzymatic-assisted multimodal MS imaging, which enhances the detection of large biomolecules and improves the sensitivity of low-abundance analytes. Chemical derivatizations have also been employed to enhance ionization efficiency and eliminate endogenous interferences, improving the visualization of specific molecules. The integration of artificial intelligence (AI) methods with MSI data analysis has also been explored, offering more comprehensive insights from biological samples. AI methods such as deep learning and neural networks have been used to analyze MSI data, improving the accuracy and efficiency of data analysis. Overall, MSI is a powerful tool for spatially resolved multi-omics molecular mapping, providing valuable insights into the molecular intricacies of living systems. The continued advancements in MSI technology are expected to further enhance the capabilities of this technique, enabling more comprehensive and accurate analysis of biomolecules in biological samples.