Pakala Chandra Sekhar , M Reddy Varun , C Vinay , P Pawan Kalyan , S Prashanth 1 4 (October-December) This study proposes an innovative method for detecting and grading diabetic retinopathy using publicly available fundus images By integrating image processing and machine learning it accurately identifies indicators like exudates and microaneurysms crucial for assessing the conditions severity Results show high accuracies with support vector machine and KNearest neighbor methods achieving 921 for exudate grading and decision tree model reaching 999 for microaneurysm grading This automated approach holds promise for early detection and precise grading facilitating timely intervention and improving patient outcomes Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026