Cervical cancer is a predominant cause of cancer-related mortality among women worldwide. Timely and precise diagnosis is essential for directing appropriate therapy and enhancing patient outcomes. This paper introduces a deep learning framework for the multi-class categorization of cervical cytology pictures via transfer learning methodologies. This method utilizes three pre-trained Convolutional Neural Network (CNN) architectures—ResNet50, InceptionV3, and EfficientNetB0—that were fine-tuned using a balanced variant of the Mendeley LBC dataset. This dataset consists of 963 Pap smear pictures classified into four unique categories: NILM, LSIL, HSIL, and SCC. Among the assessed models, ResNet50 exhibited the greatest classification accuracy at 97%, surpassing both InceptionV3 and EfficientNetB0. To improve model interpretability and promote clinical adoption, Grad-CAM visualization was utilized to emphasize the areas inside each picture that most substantially influenced the model’s predictions. The system was implemented using a Flask-based web application, facilitating real-time image uploads and automatic diagnostics. This renders the solution both accessible and user-friendly for healthcare professionals and patients. The proposed integrated system demonstrates robust performance and practical application, providing a dependable and interpretable AI-based tool to facilitate early detection and assist pathologists, particularly in resource-constrained environments.