The retinal vasculature has been recognized as a fundamental element in glaucoma as well as diabetic retinopathy. Segmentation of retinal blood vessels is of considerable clinical significance for diagnosing the glaucoma disease at an early stage. With the intention of glaucoma detection, initially, retinal images are acquired by utilizing advanced capture devices for image content. The present investigation has been developed for the detailed computational model analysis of the blood flow in physiologically sensible retinal arterial and venous networks. The geometrical views of both retinal artery and vein have been extricated from the blood vessels of the retinal fundus image utilizing morphological operations. The segmented arteries and veins are demonstrated utilizing the impedance-modeling method and Finite Element Analysis is utilized for the portrayal of arteries and veins to decide the biomechanical parameters of the blood that incorporates structural analysis and computational fluid analysis. The anticipated parameters are classified based on the blood flow attributes by using Support Vector Machine (SVM). The proposed technique accomplishes the maximum accuracy of 94.86% for the proficient prediction of Glaucomatous disease contrasted with existing strategies. © 2020