The state of one’s nails can be uniquely and easily accessed by examining nail deficiencies, which may be a sign of vitamin imbalances. The limits of current diagnostic approaches, which can be time-consuming and include intrusive procedures, often result in the prevalence of micronutrient deficiencies being ignored. This research offers a fresh strategy for effective detection that makes use of cutting-edge image processing methods. Precise identification of anomalies associated with micronutrients is made possible by the application of UNet-based image segmentation to fingernail pictures. Next, an extensive image categorization is carried out with the use of well-known models like VGG16, ResNet50, and Efficient Net, combining transfer learning and fine-tuning techniques. By addressing the drawbacks of conventional approaches, this integrated methodology seeks to improve the speed and accuracy of micronutrient insufficiency detection. This study uses state-of-the-art technology to help develop a non-invasive, time-saving diagnostic tool that could transform the field of nutritional evaluations and encourage early intervention for better public health outcomes.