Cervical cancer, a disease that can be prevented with early detection, remains a global health concern, mainly in the remote and underrepresented regions of the world. Our study introduces an ensemble method combining Swin Transformer, Vision Transformer (ViT), and ResNet50 to classify cancerous and precancerous cells in Pap smear images. Each model has almost unique characteristics—hierarchical vision processing, global attention mechanisms, and robust feature extraction—the ensemble uses Softmax-weighted averaging to combine the classification power of each model and reduce false positives. Our ensemble model is evaluated on the SipakMed dataset of 4,049 images and 966 manually cropped cell clusters, achieving an impressive accuracy of 97.87% outperforming many state-of-the-art models for cervical cancer classification. These results also strengthen the fact that deep learning models and ensembles have the required capability to improve diagnostic accuracy and reliability in healthcare, building the path for more efficient and trustworthy screening tools.