Integrating Quantum Convolutional Neural Networks (QCNNs) into medical diagnostics represents a transformative advancement in the classification of brain tumors. This research details a high-precision design and execution of a QCNN model specifically tailored to identify and classify brain cancer images. Our proposed QCNN architecture and algorithm have achieved an exceptional classification accuracy of 99.67%, demonstrating the model's potential as a powerful tool for clinical applications. The remarkable performance of our model underscores its capability to facilitate rapid and reliable brain tumor diagnoses, potentially streamlining the decision-making process in treatment planning. These findings strongly support the further investigation and application of quantum computing and quantum machine learning methodologies in medical imaging, suggesting a future where quantum-enhanced diagnostics could significantly elevate the standard of patient care and treatment outcomes.