“NeuroVision” is the name of a novel paper that updates the identification of various eye conditions such glaucoma, cataracts, bulging eyes, crossed eyes, and uveitis by utilising cutting-edge deep learning paradigms—CNNs, RNNs, and GANs. In order to meet the pressing demand for improved diagnostic tools in ophthalmology, the core objective of our paper is to carefully assess and compare various deep learning models in order to determine the best effective algorithm for accurate eye problem recognition. Supported by an extensive dataset that covers a variety of eye conditions, “NeuroVision” emphasises the critical need to provide simple, easy-to-use solutions focused on early diagnosis, which will greatly improve patient care in the field of ophthalmic health. A new age in eye health diagnostics is arrived at by the smooth integration of modern technological developments with domain-specific medical insights. “NeuroVision” seeks to revolutionise eye care by providing medical professionals with advanced instruments for timely and precise intervention. Our research envisions an ophthalmology landscape in which early and accurate diagnosis is the key to improving patient outcomes and where proactive identification of eye problems plays a major role in designing this future.