Despite advancements in health care, cervical cancer is still one of the most prevalent causes of death amongst women globally. This highlights the importance of early diagnosis, which significantly improves the chances of positive treatment outcomes. Traditionally, the Pap sample screening has been used; however, its interpretation is human-dependent, which causes variability and prospective delays in patient treatment. To address these issues, this study offers a model called CerviScan, which utilizes Convolutional Neural Networks, specifically InceptionV3, to automatically multiclass cervical cell image classification. The SIPaKMeD dataset, containing 5 different types of cervical cell images, served as the training dataset for our model. Through transfer learning and extensive augmentation, we sought to improve generalization capability. As a result, CerviScan was able to surpass a classification threshold of 96.8%, exhibiting remarkable accuracy and recall for all cell type categories. Concerning this medical imaging problem, InceptionV3, along with fine-tuning techniques, has deeply enhanced classification performance owing to its deep feature extraction capabilities. This system is highly accurate, cost-effective, and efficient, which offers remarkable assistance in eliminating repetitive tasks for pathologists and thereby accelerating the diagnosis process. Adapting deep learning methodologies to enhance the early detection of cervical cancer, as discussed in CerviScan, demonstrates the potential it has to improve cervix cancer detection and healthcare equality on a global scale.