The rise of mortality among women is mainly due to cervical cancer these days. So, there arises the need for an effective diagnostic tool for timely and accurate detection. Fortunately, the latest cutting-edge developments have enabled early, cost-effective, and sensitive screening of Pap smear images, distinguishing between normal and abnormal cells. This study proposes a GUI-based application employing a variant of a capsule network named DenseCapsNet for the identification of cervical cancer cells. DenseCapsNet is a hybrid architecture that combines DenseNet121 and CapsNet. The proposed framework uses Wiener filtering for noise reduction and TransUNet for image segmentation. The feature vectors are derived using Dense Nets on the real dataset obtained from a government hospital. The model is trained on 440 manually extracted and isolated cervical cell images and achieved an accuracy of 97.27%, a precision of 98.15%, and a recall of 96.36%. The system handles critical issues in medical image analysis, such as cell overlap and noise artifacts. It provides a user-friendly solution for clinicians. Overall, this study highlights the potential of AI in efficient cervical cancer diagnosis and suggests future integration with multi-modal imaging to improve diagnostic accuracy.