Vitamin deficiencies are serious health risks, affecting overall well-being and leading to a variety of illnesses. This article describes a deep learning-based solution that employs the Dense Net model to detect vitamin deficiencies in medical photos and make individualized dietary recommendations. The system uses images of specific body parts, such as the eyes, lips, tongue, nails, and skin, which can show visible signs of vitamin deficiencies. It makes use of Dense net powerful feature extraction capabilities to achieve accurate classification. The system utilizes Dense net model for detecting vitamin deficiency and recommendation, while this model achieves a detection accuracy of 97%. When a deficiency is found, the system provides specialized food recommendations to remedy that specific need. This integrated strategy combines computerized diagnosis with actionable dietary advice, resulting in an effective tool for controlling vitamin deficiencies and improving health outcomes.