Diabetic retinopathy (DR) represents one of the most serious complications associated with diabetes mellitus, posing a significant threat to vision and leading to severe impairment and potential blindness if not diagnosed and treated promptly. The study investigates the integration of advanced edge detection techniques with machine learning algorithms to enhance the precision and effectiveness of DR diagnosis. By leveraging the APTOS 2019 Blindness Detection dataset, the research employs a combination of edge detection methods such as the Sobel operator and the Canny edge detector, alongside advanced preprocessing techniques and sophisticated feature extraction methods. The study reveals that the synergy between these edge detection techniques and machine learning significantly boosts the diagnostic accuracy of neural networks. Specifically, the accuracy for multiclass classification (spanning five categories: No diabetic retinopathy, Mild, Moderate, Severe, and Proliferative diabetic retinopathy) improved from 78.5% to an impressive 88.2%. This marked enhancement underscores the potential of these techniques in refining the diagnostic processes for early DR detection. By improving the accuracy of classification, this approach not only facilitates early intervention but also plays a crucial role in reducing the risk of severe vision loss among patients with diabetes. The findings of this study emphasize the importance of integrating advanced image processing techniques with machine learning frameworks in medical diagnostics. The improved outcomes demonstrated in this research highlight the potential for such technological advancements to contribute meaningfully to the field of ophthalmology, leading to better patient care and potentially transforming the standard of practice in DR diagnosis.