Abstract: Mass spectrometry imaging (MSI) has emerged as a powerful technique for label-free molecular mapping in biological tissues, enabling spatial analysis of small molecules at the single-cell level. MSI combines spatially resolved tissue sampling with high sensitivity, facilitating the visualization of molecular distributions. This review summarized advancements in the main MSI techniques: matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), secondary ion mass spectrometry imaging (SIMS-MSI), and desorption electrospray ionization mass spectrometry imaging (DESI-MSI), which have evolved to offer improved resolution and sensitivity for small molecule analysis in complex biological environments. MALDI-MSI, widely used for its capability to analyze complex biomolecules, has recently advanced with the development of t-MALDI-2 ion sources and laser post-ionization techniques. These innovations have enabled subcellular resolution (down to 1 μm) and deeper molecular coverage in single-cell studies, enhancing ion yield and detection sensitivity for complex molecules. SIMS-MSI, recognized for its high spatial resolution (down to 25 nm), has been improved through gas cluster ion beam (GCIB) technique, which increases ion yield and facilitates detailed analysis of low-abundance metabolites. SIMS-MSI is particularly suited for applications requiring ultra-high precision, such as mapping cellular organelles and metabolic profiling. DESI-MSI operates under ambient conditions and has recently achieved improved spatial resolution (down to 6 μm), making it applicable for live-cell analysis and rapid tissue diagnostics. The integration of microfluidic probes and tapping-mode scanning has enhanced the spatial resolution and ion detection efficiency of DESI-MSI, expanding its capabilities in clinical and real-time molecular studies. Despite of these advancements, challenges still remain, including the trade-off between spatial resolution and sensitivity, particularly for low-abundance compounds. Sample preparation methods, such as those involving vacuum or cryogenic conditions, may introduce artifacts and affect physiological relevance. Future developments of ambient-compatible environments and AI-driven data analysis workflows are critical to overcome these limitations. In conclusion, MSI, especially at the single-cell level, is transforming spatial metabolomics and holds great promise in cancer research, neuroscience, and drug development. Continued innovations in both hardware and analytical techniques will drive breakthroughs in understanding cellular heterogeneity and advancing precision medicine.