The paper aims to develop a more accurate cervical cancer (CC) detection method. As a consequence of this, the data’s dependability will increase. As a direct result, gathering new biomedical data has emerged as a primary goal. The three most significant high-dimensional biological data analytics that may be conducted using bioinformatics are sample classification, early disease detection, and disease diagnosis. This paper provides an earlier data-feature selection technique based on stability (EDFSS). In addition, we construct a two-stage heterogeneous stacked ensemble learning model (HSELM) for predicting cervical cancer patient survival. Both models are designed to predict whether a patient will survive their disease. EDFSS discovers feature subsets from large cancer datasets that are particularly valuable for developing other models. The usage of this strategy is predicted to result in enhanced classification accuracy. The CC Risk Factors, 32 independent risk factors, and four dependent variables were used in this study. This also made extensive use of the CC Risk Factors. These strategies were presented to sanitize the risk variables contained in the previous algorithm. This was done to improve the algorithm’s efficacy. The MLP classifier exhibited the best overall classification performance, with 97.51% accuracy, 96.23% precision, 98.10% recall, and 95.21% f1-measure. The paper found that risk variables were selected to train several ML classifiers to predict cervical cancer.