This study presents a machine learning framework to classify and predict cancer drug combinations as synergistic, additive, or antagonistic. The framework involves 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, 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, such as kinase inhibitors with mTOR, DNA damage-inducing drugs, or HDAC inhibitors, particularly for ovarian, melanoma, prostate, lung, and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776, and AZD1775 as frequently synergizing across cancer types. The framework provides a valuable approach to uncover more effective multi-drug regimens for cancer treatment. The study also discusses related work in drug combination research, highlighting the need for tools to identify optimal drug pairs for effective and synergistic cancer treatment. Recent advancements in machine learning and biomedical science have enabled data-driven assessments of diseases, with machine learning algorithms playing a key role in extracting meaningful conclusions from big data. The main objective of this paper is to utilize machine learning to predict effective drug synergy pairs for cancer treatment. The proposed approach includes data collection and annotation, data preprocessing, partitioning the dataset into training and test sets, building classification and regression models, testing, and validating the most suitable models. Additionally, it examines drug features and mechanisms of action to better understand synergy behavior. The key contributions of this paper include annotating each drug combination by its generic name and mechanism of action, classifying synergism, additive, and antagonism, determining the best combination of CSS scores, classifying data by cancer type, and identifying synergistic drug combinations for each cell line. The study also discusses the performance of various machine learning classifiers and regression models, showing that the random forest regression model achieved the best performance in predicting CSS scores with an MAE of 0.09 and MSE of 0.013. The results indicate that the proposed framework can effectively identify synergistic drug combinations for cancer treatment, contributing to the development of more effective and personalized cancer therapies.This study presents a machine learning framework to classify and predict cancer drug combinations as synergistic, additive, or antagonistic. The framework involves 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, 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, such as kinase inhibitors with mTOR, DNA damage-inducing drugs, or HDAC inhibitors, particularly for ovarian, melanoma, prostate, lung, and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776, and AZD1775 as frequently synergizing across cancer types. The framework provides a valuable approach to uncover more effective multi-drug regimens for cancer treatment. The study also discusses related work in drug combination research, highlighting the need for tools to identify optimal drug pairs for effective and synergistic cancer treatment. Recent advancements in machine learning and biomedical science have enabled data-driven assessments of diseases, with machine learning algorithms playing a key role in extracting meaningful conclusions from big data. The main objective of this paper is to utilize machine learning to predict effective drug synergy pairs for cancer treatment. The proposed approach includes data collection and annotation, data preprocessing, partitioning the dataset into training and test sets, building classification and regression models, testing, and validating the most suitable models. Additionally, it examines drug features and mechanisms of action to better understand synergy behavior. The key contributions of this paper include annotating each drug combination by its generic name and mechanism of action, classifying synergism, additive, and antagonism, determining the best combination of CSS scores, classifying data by cancer type, and identifying synergistic drug combinations for each cell line. The study also discusses the performance of various machine learning classifiers and regression models, showing that the random forest regression model achieved the best performance in predicting CSS scores with an MAE of 0.09 and MSE of 0.013. The results indicate that the proposed framework can effectively identify synergistic drug combinations for cancer treatment, contributing to the development of more effective and personalized cancer therapies.