Diabetic　Retinopathy　(DR)　is　a　prevalent　outcome　of　diabetic　mellitus.　This　causes　lesions　to　form　on　the　retina,　impairing　eyesight.　Most　likely,　blindness　can　be　avoided　if　the　DR　condition　is　discovered　at　an　initial　stage.　Since　DR　is　a　non-reversible　condition,　early　detection　and　treatment　can　significantly　reduce　the　chance　of　visual　loss.　Fundus　images　manually　detect　DR,　which　is　a　laborious　and　error-prone　procedure.　In　assessing　and　categorizing　medical　images,　machine　learning　and　deep　learning　have　emerged　as　the　most　efficient　methods,　surpassing　human　performance,　common　image　processing　methods,　and　other　computer-aided　detection　systems.　For　this　article,　the　most　recent　approaches　for　utilizing　fundus　images　to　classify　and　detect　DR　using　machine　learning　and　deep　learning　methods　have　been　researched　and　evaluated.　The　freely　accessible　DR　Datasets　consisting　of　fundus　images　have　also　been　discussed.　We　reviewed　several　DR　pipeline　components,　including　the　datasets　that　researchers　frequently　used　and　the　preprocessing　and　data　augmentation　steps,　feature　extraction　methods,　commonly　used　detection　and　classification　algorithms,　and　the　generally　used　performance　metrics.　This　paper　ends　with　a　discussion　of　current　challenges　that　have　to　be　tackled　by　researchers　working　in　this　field　to　translate　the　research　methodology　into　actual　clinical　practice.　Finally,　we　conclude　with　a　discussion　of　the　future　perspectives　of　DR.