Prolonged diabetic Macular Edema (DME) is a disease especially seen in diabetic patients. It could result in blindness. Therefore, in order to avoid irreparable vision loss, early detection and treatment are essential. Images from optical coherence tomography (OCT) have been utilized extensively to aid in the detection of various illnesses. Manual screening is costly, time-consuming, and prone to human error. In order to get around these limitations, artificial intelligence (AI) approaches have been used extensively. This research contributes towards development of textual reports, helpful for ophthalmologists to increase overall performance. Here, a variety of machine learning (ML) and deep learning (DL) approaches are used to develop retinal health detection models. It is found that Convolutional Neural Networks (CNN). Models were extensively employed in DL and ML methods for the analysis of OCT images. Model is developed for detecting and analysing Diabetic macular edema using methods of artificial intelligence throughout the past ten years. We have talked about the shortcomings of the current techniques and offered ideas for new approaches to precisely identify and confirm eye conditions.