Mass spectrometry-based single-cell metabolomics (SCM) reveals the inherent heterogeneity of individual cells among seemingly identical cell types. Fast-scanning and high-resolving mass analyzers provide the sensitivity and specificity required to probe minuscule amounts of biological material. However, acquiring data from hundreds of individual cells to achieve statistical power results in complex data sets. This challenge is compounded by the limited availability of specialized data analysis tools for single-cell metabolomics, as many techniques depend on the use of specialized sampling and ionization probes. This results in incompatibility with conventional metabolomics data processing tools. Here, we present CellMate, a MATLAB-based data processing platform designed for single-cell metabolomics using direct infusion techniques. CellMate comprises identification and peak alignment of detected metabolites in an intuitive graphical user interface. CellMate supports customizable quantitative, targeted, and nontargeted metabolomic workflows. The untargeted workflow is enabled by a novel deep learning-based image classification algorithm that effectively distinguishes endogenous metabolites from background species. The source code, along with a compiled installer, is available at github.com /LanekoffLab/CellMate. We believe that CellMate represents a significant advancement in the single-cell metabolomics toolbox, enabling comprehensive data extraction of precious metabolite information from single cells.