Many existing Deep Learning (DL)-based approaches for cervical cancer diagnosis lack comprehensive architectural comparisons and fail to effectively integrate Explainable AI (XAI) methods, such as Grad-CAM. This study proposes a cervical cancer classification framework that utilizes several state-of-the-art DL models and employs Grad-CAM to generate heatmaps highlighting the specific image regions that influenced the model’s predictions. These visual explanations improve transparency and support clinical interpretation, thereby enhancing trust in AI-assisted diagnostic systems. The experimental results demonstrate that the proposed framework not only achieves high classification accuracy but also provides valuable visual insights, contributing to the development of more interpretable and reliable AI tools for medical image diagnosis.