Due to their high dimensionality, high complexity, and large size, extracting essential biological information from MSI data is generally challenging. To facilitate the relevant studies, advanced data analysis methods have been developed and combined with the Single-probe MSI experiments. developed a data analysis method using Multivariant Curve Resolution (MCR) and Machine Learning (ML) approaches, which were then used to analyze the MSI data from mouse kidney slice. This method involved four main steps: data pre-processing, MCR-Alternating Least Squares (ALS), supervised ML (e.g., Random Forest), and unsupervised ML (e.g., Clustering Large Applications (CLARA) and Density-based Spatial Clustering of Applications with Noise (DBSCAN)). A key step was using t-SNE, a dimensionality reduction tool, to process and visualize the complex datasets. For supervised ML methods, predefined histological regions identified through MCR-ALS were used to train the models. In unsupervised methods, t-SNE prepared the data for clustering. The combination of these approaches provided a more thorough understanding of chemical and spatial features in the data. Other machine learning methods were then developed to improve the MSI data analysis. In a study involving cancer spheroids, the Single-probe was used to examine the effects of the anticancer drug Irinotecan on colorectal cancer (HCT-116) spheroids . By obtaining spatially resolved metabolomic profiles, the technique revealed how the drug affected the abundance of metabolites in different regions of the 3D tumor model. ML techniques, such as Random Forest and CLARA, were employed to analyze the MSI data, improving the identification and classification of metabolomic features.