 Cancer treatment faces many challenges, including drug resistance due to tumor cell heterogeneity. Existing datasets contain relationships between gene expression and drug sensitivities, which are mostly derived from tissue-level studies. Single-cell drug sensitivity analysis is needed to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Machine learning techniques can be used to better understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. This review explores the trends and potential of drug research at the single-cell level, with the goal of providing inspiration for further advancements in precision medicine. This article was authored by Renchi and Kwanzo.