Cervical cancer constitutes a significant public health concern and early diagnosis plays an important role in the patient's recovery. In this study, we investigated the utilization of various algorithms in machine learning to predict cancer with best accuracy. The objective of the paper is to identify the most reliable predictors of cervical cancer through comparative analysis. To achieve this goal, we obtained information including medical and demographic characteristics of different patients. The data has been prepared for analysis by addressing any missing values, normalizing features, and by resolving intra-class imbalance. We used algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Decision Tree, Random Forest, XG Boost etc. Metrics like precision, accuracy, and recall, and area under receiver operating characteristic curve (AUC-ROC) are used for evaluating accuracy and discrimination. Performance of these models is also compared to real-world applications. We highlight significance of machine learning algorithms in early prediction of cervical cancer. Among all the models used, XG Boost is getting higher accuracy of 99.22%. These findings provide valuable insights to researchers, physicians and policy makers, leading to ways to enhance care for patient and to mitigate the global impact of cervical cancer worldwide.