Diabetes mellitus is the root cause of a disease known as diabetic retinopathy, which is a disorder that affects the retina. In every region of the globe, it is the leading cause of blindness. Early detection and treatment are very necessary in order to delay or avoid the deterioration of vision and the loss of eyesight. The scientific community has proposed a number of artificial intelligence algorithms for the aim of identifying and classifying diabetic retinopathy in fundus retina pictures due to the fact that this is the intended objective. Utilizing a Convolutional Neural Network (CNN), we suggested a method for the identification and early prediction of diabetic retinopathy in this particular piece of research. Using a wide variety of hyperparameters, such as epoch size, batch size, optimized, and so on, the Deep CNN has been used for both training and testing purposes. Examples of normal and abnormal retinal pictures have been included in the MRI dataset. When the findings of the experimental investigation were evaluated using machine learning and deep learning algorithms like SVM, ANN, and CNN, the results were shown to be accurate. In conclusion, the CNN achieves a detection and prediction accuracy of 96.60%, which is superior to that of the SVM and other artificial neural networks.