Proposed　novel　investigation　focused　on　leveraging　an　innovative　diabetic　retinopathy　(DR)　dataset　comprising　seven　severity　stages,　an　approach　not　previously　examined.　By　capitalizing　on　this　unique　resource,　this　study′s　findings　set　a　new　benchmark　for　DR　classification,　highlighting　the　transformative　potential　of　incorporating　advanced　data　into　AI　models.　This　study　developed　a　Vgg16　transfer　learning　model　and　gauged　its　performance　against　established　algorithms　including　Vgg-19,　AlexNet,　and　SqueezeNet.　Remarkably,　our　results　achieved　accuracy　rates　of　96.95,　96.75,　96.09,　and　92.96,　respectively,　emphasizing　the　contribution　of　our　work.　We　strongly　emphasized　comprehensive　severity　rating,　yielding　perfect　and　impressive　F1-scores　of　1.00　for　＇mild　NPDR＇　and　97.00　for　＇no　DR　signs.＇　The　Vgg16-TL　model　consistently　outperformed　other　models　across　all　severity　levels,　reinforcing　the　value　of　our　discoveries.　Our　deep　learning　training　process,　carefully　selecting　a　learning　rate　of　1e-05,　allowed　continuous　refinements　in　training　and　validation　accuracy.　Beyond　metrics,　our　investigation　underscores　the　vital　clinical　importance　of　precise　DR　classification　for　preventing　vision　loss.　This　study　conclusively　establishes　deep　learning　as　a　powerful　transformative　tool　for　developing　effective　DR　algorithms　with　the　potential　to　improve　patient　outcomes　and　advance　ophthalmology　standards.　©　2024　Wiley　Periodicals　LLC.