Cervical cancer is the leading cause of cancer related death among women worldwide and hence screening tools as well as risk prediction markers are very much needed. This study explored the prediction capacity of machine learning algorithms for cervical cancer risk factors with a large dataset. Description provided by the dataset: The data show information on diagnostic features, demographic data, and a clinical history of patients undergoing cervical cancer screening. There are two basic steps involved in data preprocessing, i.e., handling missing values, and standardizing numerical features pair plots and statistical summaries are the two approaches for exploratory data analysis (EDA) which can be used to understand how variables are distributed individually, or their relevance. The four machine learning models—XGBoost, Decision Tree, Random Forest, and Logistic Regression—are trained and assessed using GridSearchCV’s hyperparameter optimization. Performance metrics are used to evaluate how well the model predicts the risk of cervical cancer. These metrics include accuracy, precision, recall, and F1-score. Among the best are the Random Forest and XGBoost models, which demonstrate strong classification abilities and emphasize important variables like age, metrics related to sexual behavior, and cytological results. Random Forest achieved 94% accuracy, 60% precision, 54% recall, and 57% F1-score in contrast to XGBoost’s 94% accuracy, 53% precision, 63% recall, and 58% F1-score statistics. By integrating these models into an ensemble of voting classifiers, the predictive accuracy and reliability are improved for a variety of patient profiles.