in understanding drug resistance mechanisms, predicting a drug-resistant phenotype before starting chemotherapy remains underexplored, which can result in ineffective treatments and unwanted toxicity for patients. For the first time, the integration of the Single-probe SCMS with machine learning techniques was performed by Liu et. al to quickly and accurately predict the phenotypes of unknown single cells. This innovative approach, facilitated by the Single-probe, offers a solution for the rapid and reliable prediction of drug-resistant cancer cell phenotypes such as those associated with chemoresistance mechanisms (e.g. cell adhesion-mediated drug resistance (CAM-DR)) . Advanced data analysis, incorporating machine learning algorithms, was subsequently used to process complex metabolomic data. Specifically, random forest (RF), penalized logistic regression (LR), and artificial neural networks (ANN) were used for analyzing pre-treated single cell metabolomic datasets. By integrating a diverse range of cellular metabolites, the models achieved significantly improved predictive accuracy (p-value < 0.05) compared to other approaches that relied solely on metabolic biomarkers identified through two-sample ttests or PCA loading plots. This highlights the effectiveness of our methodology in enhancing model performance.