Pancreatic cancer is a fatal illness, characterized by asymptomatic advancement and late-stage detection, rendering conventional machine learning models ineffectual. A Hybrid Quantum-Classical Neural Network (QCNN) is suggested to merge quantum computing with deep learning (DL) for the prediction of pancreatic cancer. The approach integrates intricate medical data into quantum states, enabling enhanced feature extraction and superior pattern identification. The quantum layer is then integrated with a traditional DL network, enhancing these properties for precise categorization. The system utilizes TensorFlow Quantum (TFQ) and IBM Qiskit, allowing quantum-enhanced data processing in a conventional deep-learning pipeline. Experiments on simulated medical datasets demonstrate encouraging efficacy in the classification of pancreatic cancer cases. The quantum element facilitates the effective management of high-dimensional features, possibly surpassing traditional models in generalization and feature differentiation. Subsequent studies will concentrate on enhancing quantum circuit topologies, broadening dataset variety, and evaluating the model using authentic clinical data utilizing quantum hardware.