Diabetic Retinopathy (DR) is an eye disease which occurs due to enormous glucose level in the blood among diabetic patients. The diabetic patients are having higher chances of getting blindness if the sugar level is increased in body. An identification of landmark features present in the fundus images has to accurately find the features from the optic disc of fundus images. The existing researches used various Artificial Intelligence (AI) techniques for screening and diagnosing the DR earlier to prevent the diabetic patients from blindness which was determined based on the level of DR severity. However, the existing models were efficient that consumed time and the premature convergence was resulted with the drawback for the real world optimization approach. Thus, the proposed Dynamic weighted based Cuckoo Search Algorithm (DWCSA) is modified to improve the performances based on their basic structure. The higher convergence helped to train the model better for finding solutions. The proposed model overcomes the constraint issues occurred during feature selection process showed improvement in the accuracy. The proposed DWCSA obtained the accuracy of 97.46 % compared to the existing CNN model that obtained 98.94 % of accuracy for DIARETDB1 whereas, the eophtha dataset obtained accuracy of 98.91% for the proposed model.