Diabetic Retinopathy (DR) has emerged as a grave public concern in recent years, which leads to vision impairment and blindness. This progression of DR as a public health concern is because is typically asymptomatic in its early stages therefore making it excruciating to detect in early stages. This study explores the use of ResNet-based Quantum Transfer Learning for DR detection, combining classical deep learning with quantum computing to enhance diagnostic accuracy. This hybrid approach demonstrates the potential of quantum computing to augment deep learning models, particularly in handling intricate medical image data, and contributes to the growing field of quantum-assisted medical diagnostics, offering a novel perspective on the application of quantum computing in healthcare. The model’s design and its performance, shaped by the intricate behavior of quantum systems, pave the way for future research that may refine and expand upon these preliminary insights, ultimately contributing to the evolution of quantum-assisted methodologies in healthcare.