Cervical cancer remains the most common and life-threatening malignancies around the world. As with many diseases, timely and accurate intervention is critical in this case if one wishes to reduce the mortality rate associated with this medical condition. The purpose of this research is to develop an innovative computational framework to detect cervical cancer using both quantitative and data-driven approaches. The primary data set consists of the demographics, lifestyle choices, and medical history of 980 patients, which was collected at the Hospital Universitario de Caracas in Venezuela. For reproducibility purposes and computational efficiency, all model testing was performed on Google Colab, a cloud platform suitable for big data. The ensemble learning methodology integrated a convolutional neural network (CNN) with several machine learning classifiers, including Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors, Extreme Gradient Boosting, Support Vector Machine, Extra Tree Classifier, Categorical Boosting, Gradient Boosting, and Adaptive Boosting. This paper also looks at the impact of model performance on original and engineered feature selection. After extensive comparative evaluation, the proposed framework showed an extraordinary predictive performance of 98.96% classification accuracy, thus confirming the value of the framework. This showcases the enormous potential of machine learning in revolutionizing cervical cancer diagnosis. This study seeks to refine diagnostic precision in order to enable timely detection while minimizing misdiagnosis risks. Through integrated datasets, the framework deepens understanding of the influencing factors and enhances the accuracy of the predictions, providing more precise forecasts. This technology seeks to improve patient prognosis by providing more timely and effective cervical cancer diagnosis and treatment.