This study presents a machine learning framework to classify and predict synergistic combinations of FDA-approved cancer drugs. The framework involves several key steps, including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores, and examination of drug features and mechanisms of action to understand synergy behaviors. The models identified combination pairs most likely to synergize against different cancers, particularly kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs, or HDAC inhibitors. Notably, Gemcitabine, MK-8776, and AZD1775 were frequently synergizing across various cancer types. This framework provides a valuable approach to uncover more effective multi-drug regimens, enhancing the effectiveness of combination therapy and improving patient outcomes.This study presents a machine learning framework to classify and predict synergistic combinations of FDA-approved cancer drugs. The framework involves several key steps, including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores, and examination of drug features and mechanisms of action to understand synergy behaviors. The models identified combination pairs most likely to synergize against different cancers, particularly kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs, or HDAC inhibitors. Notably, Gemcitabine, MK-8776, and AZD1775 were frequently synergizing across various cancer types. This framework provides a valuable approach to uncover more effective multi-drug regimens, enhancing the effectiveness of combination therapy and improving patient outcomes.