Diabetic Retinopathy (DR) is a prevalent issue among diabetic individuals, resulting in a constructive loss of vision in such people. If this problem is not diagnosed early on, there is no therapy available to restore vision. As a result, the only way out of this unreversible condition is to diagnose the sickness early on and cure it. The ophthalmologists use the "fundus images" of the patients' eyes, which are the retinal pictures of the patients, to maintain their eyesight. However, detecting an abnormality in a human eye with the naked eye of another person takes time, money, and can occasionally lead to misjudgment owing to subjective differences and concerns among ophthalmologists. As a result, we employ "Deep Learning" algorithms to diagnose diabetic retinopathy using fundus pictures in this article. As a result, a computer-aided diagnosis system is established, which leads to a reduction in misdiagnosis. Deep learning approaches have recently become the most popular ways for improving image recognition or feature detection systems' accuracy for both classification and regression. In this study, we employ Convolutional Neural Networks (CNN) for image identification, training the neural network model with retinal pictures, and achieving excellent accuracies. In this work, the difficulties of various methodologies as well as faults with existing methods were examined.