The application of machine learning to characterization is a key technology in Materials Informatics (MI) and Lab Automation (LA) for rapidly and accurately evaluating the properties of materials and samples. In this article, we introduce data-driven analytical approaches that employ machine learning and deep learning to address challenges inherent in mass spectrometry analysis using GC‐MS and LC‐MS, such as the vast amount of data, variability in peak shapes, retention time shifts, and peak overlaps. These approaches enable automated and accelerated pre-processing, enhanced reproducibility, and the identification of unknown components, thereby significantly improving the efficiency and accuracy of analyses.