Diabetic retinopathy (DR) is the main cause of vision loss in individuals with diabetes. Early detection and timely intervention are crucial in preventing irreversible vision loss. One of the key challenges in diabetic retinopathy detection is the accurate classification and identification of retinal images based on their severity levels. To address this challenge, deep learning techniques have emerged as powerful tools in automated diabetic retinopathy detection. These techniques, including convolutional neural networks (CNN) such as AlexNet, VGG19, InceptionV3, ResNet18, and DenseNet121, have been broadly studied and explored in various research papers. Also giving promising results in accurately classifying DR based on retinal images. They have shown high sensitivity and specificity in detecting the presence and severity of DR lesions, such as microaneurysms, which are common signs of diabetic retinopathy. Deep learning models have also been used to develop browser-based applications that provide real-time predictions and probabilities associated with each class. These developments in deep learning techniques have revolutionized DR detection, providing a more efficient and accurate approach than traditional manual methods. The promise of deep learning in DR detection extends beyond classification tasks. Advanced techniques such as transfer learning and generative adversarial networks, have been introduced to further enhance the performance of models in more specialized scenarios. Transfer learning, for instance, allows for the fine-tuning of pre-trained models on a smaller dataset specific to diabetic retinopathy, leading to improved accuracy with less data. This approach is particularly beneficial given the challenges of acquiring large, annotated datasets in the medical field.