Cervical cancer is a disease that affects women and has a high fatality rate. Risk factors may help in advancing the cervical cancer early detection approach. Nonetheless, screening for cervical cancer at an earlier stage may reduce the risk of death and other complications. Regrettably, existing prediction algorithms need clinically relevant physiological and biochemical features, limiting their use to a narrower situation. To improve diagnostic, that would use a feature set with a decreased probability of occurrence in conjunction with three ensemble-based classification algorithms. The paper focuses on cervical cancer detection, which employs the advanced machine learning approach stacked unified machine learning (SUML) to improve the prediction models' performance. Stacking suitable machine learning employs a different set of learning algorithms. The screening data were arbitrarily divided into two groups: Training data accounted for 80% of the total and was used to construct the algorithm; testing data accounted for 20% of the total and evaluated the methods' validity. The random forest (RF) model and AdaBoost were employed to classify cervical cancer prognostic indicators. Furthermore, in previous cervical cancer detection studies that used fewer risk indicators, the accuracy of the recommended models is significant. As part of this research, we chose three of the most well-known machine learning algorithms and evaluated their accuracy in predicting cases of cervical cancer.