30.0 Cervical cancer, like many other cancers, is most treatable when detected at an early stage. Using classification methods helps find early signs of cancer and small tumors. This allows doctors to act quickly and offer treatments that might cure the cancer. This study presents a comprehensive approach to the classification of squamous cell carcinoma (SCC) leveraging a dataset comprising 1140 single-cell images sourced from Herlev. A variety of deep learning models, including DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101, are employed both individually and in ensembles, demonstrating their efficacy in classifying diverse cellular features. To validate the robustness of results, k-fold cross-validation is conducted, further affirming the effectiveness of the proposed methodology. Thorough exploration produces a precise and effective model for SCC classification, providing detailed insights into both normal and abnormal cell types. These findings show that transfer learning-based deep neural networks and ensemble methods can improve the diagnostic capabilities by 98% accuracy, of SCC classification systems for different cell types.