Diabetes is an illness that causes a multiple number of complications, Diabetic Retinopathy being one of them. The high level of glucose in the bloodstream eventually causes damage to the blood vessels and narrowing, including the small blood vessels in the eyes. If left unattended, this causes changes in the retina, and it can affect vision and lead to blindness. The present study was conducted to review the most predominant methods of detection and diagnosis of Diabetic Retinopathy from the years 2013 to 2023. The selection of the documents was done using the Scopus database, and the data gathered was then processed using RStudio and Google Collaborate. After processing the 9610 documents that were gathered, it was shown that with the technological advancements in recent years, new ideas and ways of detecting and diagnosing diabetic retinopathy have been developed. These include ways to get images of the fundus of the eye and process them more efficiently through methods like the use of artificial intelligence or neural networks and deep learning, the construction of hardware using microcontrollers and the use of smartphone cameras to capture the image in order to lower the costs. The use of artificial intelligence for image processing is most dominant in current trends, but the work on the development of new hardware built for image capture is not as numerous in comparison.