Cervical cancer remains a significant global health concern, posing one of the most prevalent threats among cancers worldwide. The frequency of cervical cancer occurrence ranks high among prevalent female disorders. Detecting cervical cancer early can not only save lives but also reduce the financial burden on those affected by the disease. The integration of artificial intelligence into medical decision-making is believed to enhance the accuracy of disease prediction and diagnosis, leveraging high-resolution imaging and next-generation sequencing (NGS) technologies. Artificial intelligence has the potential to revolutionize cancer diagnosis by leveraging new biomarkers identified through advanced algorithms. This breakthrough could significantly impact cancer treatment and overall health care. The authors introduce a framework that can be used for identification of the early occurrence of cervical cancer based on symptoms alone. Various other researchers have employed diverse machine learning methods like support vector machine (SVM), ensemble classifiers, K-nearest neighbor (KNN) in achieving accuracy rates. However, the authors have considered algorithms such as Naïve Bayes, AdaBoost, and decision tree in this study focusing on cervical cancer classification and demonstrated accuracies of 96%, 94%, and 98.74%, respectively. Among these, decision tree emerged with the highest accuracy of 98.74%, coupled with precision rates of 99% and robust scores in recall and F1-score.