A study demonstrates that machine learning can significantly improve the precision of distinguishing between precancerous and cancerous human epithelial cervical cells using high-resolution atomic force microscopy (AFM) imaging of adhesion maps. The research, which utilized the random forest decision tree algorithm and K-fold cross-validation, showed an increase in the area under the curve (AUC), accuracy, sensitivity, and specificity compared to previous methods. This advancement is particularly important for clinical practice, as it could improve cervical cancer screening and reduce the number of unnecessary invasive biopsies.