This study proposes a novel approach to improve cervical cancer prediction by combining three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN imputer to manage missing values. The proposed model achieves high accuracy (0.9941), precision (0.98), recall (0.96), and F1 score (0.97). The research evaluates three scenarios: missing value deletion, KNN imputation, and PCA-based imputation. The study highlights the importance of handling missing data in cervical cancer datasets and demonstrates the effectiveness of the proposed ensemble model in improving diagnostic accuracy. The study also compares the performance of various machine learning models, including RF, LR, GBM, GNB, ETC, SVC, DT, and SGD, and shows that the proposed ensemble model outperforms these models in terms of accuracy, precision, recall, and F1 score. The research addresses the limitations of traditional methods and emphasizes the potential of machine learning in cervical cancer detection. The study also discusses the limitations of the KNN and PCA imputation techniques, including sensitivity to the value of K, the curse of dimensionality, handling categorical data, interpretability, and scalability. The study concludes that the proposed ensemble model is a robust solution for cervical cancer detection, with high accuracy and the potential to improve the overall effectiveness of testing procedures. The research contributes to the field of cervical cancer detection by providing a comprehensive solution for handling missing data and improving diagnostic accuracy.This study proposes a novel approach to improve cervical cancer prediction by combining three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN imputer to manage missing values. The proposed model achieves high accuracy (0.9941), precision (0.98), recall (0.96), and F1 score (0.97). The research evaluates three scenarios: missing value deletion, KNN imputation, and PCA-based imputation. The study highlights the importance of handling missing data in cervical cancer datasets and demonstrates the effectiveness of the proposed ensemble model in improving diagnostic accuracy. The study also compares the performance of various machine learning models, including RF, LR, GBM, GNB, ETC, SVC, DT, and SGD, and shows that the proposed ensemble model outperforms these models in terms of accuracy, precision, recall, and F1 score. The research addresses the limitations of traditional methods and emphasizes the potential of machine learning in cervical cancer detection. The study also discusses the limitations of the KNN and PCA imputation techniques, including sensitivity to the value of K, the curse of dimensionality, handling categorical data, interpretability, and scalability. The study concludes that the proposed ensemble model is a robust solution for cervical cancer detection, with high accuracy and the potential to improve the overall effectiveness of testing procedures. The research contributes to the field of cervical cancer detection by providing a comprehensive solution for handling missing data and improving diagnostic accuracy.