Women in impoverished countries are disproportionately affected by cervical cancer, which is a foremost national health concern worldwide. To stop it in its tracks, early diagnosis and good care are essential. For the purpose of improving diagnostic accuracy and optimizing patient treatment techniques for cervical cancer prediction, this study utilizes ensemble learning algorithms—AdaBoost, XGBoost, CatBoost, and LightGBM. Critical parameters including accuracy, precision, recall, and F1-score are subjected to thorough examination via cross-validation in the SIPaKMeD Database from Kaggle. XGBoost achieved an outstanding 99.7% accuracy, 96.4% precision, 97.5% recall, and 96.0% F1 score, making it the best performance. The findings show that ensemble learning algorithms may work together to improve cervical cancer predictions, which might lead to better clinical outcomes with earlier diagnosis and more precise treatment. © 2025 Elsevier B.V., All rights reserved.