Diabetic retinopathy (DR), a progressive eye disease caused by diabetes, affects more than 100 million people worldwide, leading to a substantial risk of blindness if not detected early. This paper presents an innovative approach to DR detection using a fine-tuned TriNet model, designed to enhance diagnostic accuracy and efficiency. The TriNet model, a combination of three state-of-the-art models, ResNet50, DenseNet201, and VGG16 was refined to leverage the strengths of individual models and significantly enhance overall performance. Our model was trained on a comprehensive dataset divided into five classes: No DR, Mild, Moderate, Severe, and Proliferate DR. The architecture of the TriNet model was meticulously designed to improve its performance. Data preprocessing and key augmentation techniques were used to improve model robustness and generalization. Fine-tuning the TriNet model to significantly enhance accuracy involved the integration of advanced layers, dropout, batch normalization, and Adam optimization. The model achieved an accuracy of 96.47%, significantly surpassing individual models and benchmarks. Precision, recall, and F1-score values of 95.91, 96.44, and 96.17%, respectively, underscore the model’s reliability in correctly diagnosing various stages of diabetic retinopathy and minimizing misdiagnoses. The proposed TriNet model demonstrates substantial potential for large-scale usage in healthcare, facilitating prompt diagnosis and intervention, thereby improving patient well-being.