Abstract: As the fundamental unit of life activities, the single-cell serves as the core of biological systems, and its metabolic state directly and accurately reflects various physiological processes and pathological changes within the cell. Mass spectrometry (MS) technology, with its distinctive analytical characteristics of high sensitivity, high resolution, and wide analytical coverage, along with its excellent performance in both structural identification of molecules and quantitative analysis of compounds, provides powerful and solid technical support for single-cell analysis. Compared with bulk cell population analysis or fixed single-cell detection methods, live single-cell MS analysis can faithfully preserve real-time dynamic metabolic information. This advantage makes it an irreplaceable tool for revealing the heterogeneity of disease microenvironments and guiding the development and implementation of precision medicine. At present, research and development of live single-cell MS methods mainly focus on continuous innovations and improvements in sample pretreatment techniques, which is critical for ensuring analytical accuracy and reproducibility. Based on the differences in single-cell capturing and sampling methods during analysis, current live single-cell MS techniques can be broadly divided into three major categories. These include direct probe sampling of live cells, mass cytometry-based sampling of live cells, and laser-assisted desorption for single-cell imaging. Each category represents a distinct technical approach with specific advantages and application scenarios. This article systematically summarized and reviewed recent advances in live single-cell mass spectrometry methods based on the above three categories, further discussing their typical applications in key fields, such as metabolite analysis, biomarker discovery, and cancer research. In addition, this review presented perspectives on the future development of live single-cell MS technology, including improving sampling precision, enhancing detection sensitivity, increasing spatial resolution, and achieving better integration with other single-cell analysis technologies to gain a more comprehensive understanding of live single-cell metabolism.