<p>Cervical cancer, the fourth most prevalent cancer worldwide, often faces diagnostic challenges due to limited healthcare training, prompting increased interest in machine learning approaches for improved detection and classification. Current AI models for cervical cancer detection suffer from the limitations of poor generalizability, extensive computational time, and inadequate accountability. In addition, these AI models are not meticulously validated on multiple open-source cervical cancer datasets. The proposed methodology introduces a novel reinforcement learning-based successive fractionating hyperparameter optimization algorithm (SFA) employed on the random forest classifier (RF), which aims to efficiently and accurately identify cervical cancer by analyzing medical data. The proposed work is validated on the three publicly available datasets. These three datasets are pre-processed to extract key features, and hyperparameters–including maximum tree depth, number of estimators, minimum sample splits, and leaf size–are optimized using a dynamically varying fractionating factor in the SFA algorithm. The proposed framework achieved 99%, 99%, and 98% for Dataset 1, Dataset 2, and Dataset 3, respectively, using the data-splitting and 5-fold cross-validation technique. The proposed method outperforms the current state-of-the-art algorithms by 1.27% and 1%, respectively, for Dataset 1 and Dataset 2, and reduces computational time by managing the resource allocation, demonstrating its superior performance in comparison to previous methods. Despite improved accuracy, the black-box model’s interpretability and accountability are questioned, so a novel interpretable methodology, comprising SHAP (Shapely Additive Explanation), ELI5 (Explain Like I’m 5), and the Morris approaches, has been proposed. An efficient and explainable cervical cancer diagnosis system is developed to enhance feature relevance and accuracy, reducing diagnostic delays and improving treatment outcomes.</p>