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The main factor causing blindness in countries like India and others is Diabetic Retinopathy. Millions of people around the world are affected by DR, which causes vision loss and blindness. Detecting diabetes mellitus early plays an essential role in preventing the loss of vision that can result from diabetes mellitus being left untreated for a considerable amount of time. It is becoming increasingly more important to detect diabetic retinopathy early through automated systems instead of manual screening procedures like fluorescein angiography, optical coherence tomography etc., as it can lead to blindness among patients with uncontrolled diabetes. There are several published studies on machine learning and deep learning-based DR detection systems. In this paper, we explore the basics of advanced AI technologies used in DR analysis and early detection. The purpose of this study is to review DR detection methods are examined from a variety of perspectives. It includes fundus datasets, pre-processing of images, methodologies, machine learning and deep learning-based methodologies, and performance metrics. As well as presenting the review findings, it also presents the authors' observations. There were numerous public datasets for DR detection that are readily available. The Artificial Neural Network proved to be the better classifier over other machine learning methods based on shape, texture, and statistical features for DR detection. The purpose of this study is to propose the Convolutional Neural Network (CNN) through GoogLeNet for betterment based on the previous CNN model which was implemented using deep learning. Furthermore, the scientific community dedicated to developing automated DR investigative techniques. This review offers a thorough overview of DR detection methods. [ABSTRACT FROM AUTHOR]