The adoption of machine learning (ML) and deep learning (DL) technologies is rapidly expanding across various fields, particularly in healthcare, where these tools are used to analyze large datasets and uncover meaningful patterns. ML, in particular, is proving to be a powerful asset in predicting critical health issues such as cancer, kidney disease, and heart conditions. Among these, cervical cancer remains a major health challenge for women globally, where early detection can significantly improve treatment outcomes. This study presents an innovative ML-based framework for cervical cancer prediction, organized into four key stages: data acquisition, preprocessing, predictive model selection, and system implementation through pseudo-code. During the model selection phase, several prominent ML algorithms were evaluated, including Random-Forest, Gradient-Boosting, Adaptive-Boosting, Decision-Tree, Logistic-Regression, K-Nearest Neighbors, Support-Vector-Machine, and XG-Boost. The experimental results revealed that Random-Forest, Decision-Tree, Adaptive-Boosting, and Gradient-Boosting achieved perfect classification accuracy (100%), while SVM followed closely with an accuracy of 99%. The study also analyzed the computational efficiency of each algorithm. Furthermore, a survey conducted with 132 participants from Saudi Arabia explored public understanding and receptiveness toward ML-driven cervical cancer prediction tools, emphasizing awareness of the link between human-papilloma-virus (HPV) and cervical cancer development.