High blood sugar levels are what lead to diabetes. Numerous illnesses, including heart conditions, kidney problems, nerve damage, and eye impairment, can be brought on by diabetes. Diabetic retinopathy is one such complication brought on by diabetes that, if not treated or detected in a timely manner, may also result in vision loss. By training algorithms on retinal images to recognize specific features, categorize the presence or absence of the condition, or divide the image into discrete parts, machine learning can be used to recognize and diagnose diabetic retinopathy. Support Vector Machine, logistic regression, convolutional neural network, K-Nearest-Neighbor, and random forest are the current techniques utilized to identify diabetic retinopathy. The most often used deep learning methods for image detection are convolutional neural networks. To perform image classification tasks, a Convolutional Neural Network (CNN) architecture known as VGG16 was trained on a sizable dataset of images. For image classification problems, a well-known deep learning architecture is VGG16. The images are classified using the retrieved features using a variety of ML algorithms, such as KNN, SVM, Logistic Regression, Boost, AdaBoost, Decision Tree, Voting Classifier and other algorithms. This methodology is used to group diabetic retinopathy into one of five severity-based classifications (0,1,2,3,4). The proposed system will facilitate the removal of ambiguous diagnoses done by ophthalmologists. This would enable the faster and more accurate prediction and diagnosis of patients’ condition.