We demonstrate a successful use of quantum machine learning in the medical domain. This study focuses on a classification issue employing quantum transfer learning for the identification of histopathological pancreatic cancer. numerous transfer learning models, including VGG-16, ResNet18, AlexNet, Inception-v3, and numerous highly expressible variational quantum circuits (VQC), are used in this work model instead of a single one. Consequently, we offer a comparative evaluation of the models, highlighting the top-performing transfer learning model for histopathological cancer diagnosis, which has a prediction AUC of around 0.93. Additionally, we noticed that Classical (HQC) and Hybrid Quantum offered a little higher accuracy (0.885) than classical (0.88) for 1000 photos using Resnet18.