This study addresses the challenge of handling missing data in cervical cancer datasets, which is a critical issue for the development of automated diagnosis systems. The research proposes a novel approach that combines three machine learning models (XGBoost, Random Forest, and Extra Tree Classifier) into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves high accuracy, precision, recall, and F1 score, with values of 0.9941, 0.98, 0.96, and 0.97, respectively. The study examines three scenarios: deleting missing values, using KNN imputation, and employing PCA for imputation. The results show that the proposed model outperforms other methods, including those that use KNN or PCA imputation alone. The study highlights the importance of addressing missing data in cervical cancer datasets and demonstrates the effectiveness of the proposed ensemble model in improving the accuracy of cervical cancer diagnosis. This research has significant implications for medical experts, offering a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures.This study addresses the challenge of handling missing data in cervical cancer datasets, which is a critical issue for the development of automated diagnosis systems. The research proposes a novel approach that combines three machine learning models (XGBoost, Random Forest, and Extra Tree Classifier) into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves high accuracy, precision, recall, and F1 score, with values of 0.9941, 0.98, 0.96, and 0.97, respectively. The study examines three scenarios: deleting missing values, using KNN imputation, and employing PCA for imputation. The results show that the proposed model outperforms other methods, including those that use KNN or PCA imputation alone. The study highlights the importance of addressing missing data in cervical cancer datasets and demonstrates the effectiveness of the proposed ensemble model in improving the accuracy of cervical cancer diagnosis. This research has significant implications for medical experts, offering a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures.