Cervical cancer is still a leading cause of death in women, especially in low-resource environments. Early detection is important to ensure effective treatment and improved patient outcomes. The paper introduces a machine learning-based framework for cervical cancer prediction, with the optimization of the Random Forest algorithm using Genetic Algorithms. The “cervical_cancer.csv” dataset was preprocessed to deal with missing values, encode categorical data, and standardize features. This will start by evaluation of baseline models: DT, and KMeans clustering followed by the GA optimization of the RF hyperparameters.