Effective diet management is crucial for preventing non-communicable diseases, including heart disease, diabetes, and obesity. This paper introduces NutriVision, an enhanced system featuring an interactive chatbot that delivers real-time dietary guidance and personalized recommendations. NutriVision enables users to inquire about nutritional content, receive customized recipe suggestions, and track nutritional goals through natural language interaction. By integrating user data such as health conditions, dietary preferences, and nutritional requirements, it generates tailored responses that enhance user engagement and decision-making. With the help of computer vision and machine learning, NutriVision accurately identifies food items and estimates quantities from smartphone-captured images, providing instant nutritional analysis that includes both macronutrient and micronutrient details with a 94% accuracy rate. It also offers step-by-step cooking instructions, suggests dietary alternatives, and answers queries related to nutrition and health. NutriVision refines its recommendations based on user feedback, creating a dynamic and responsive dietary management experience. Unlike conventional tools that rely on manual data entry or static food databases, NutriVision delivers a seamless, AI-driven interaction by combining food recognition with personalized dialogue. NutriVision exhibits high engagement, with a user engagement rate of 87% and response accuracy of 91%, demonstrating its effectiveness in fostering sustained interaction and delivering contextually relevant and accurate information. Designed with a user-friendly interface, NutriVision supports a variety of dietary frameworks, including vegetarian, vegan, and low-carb plans, ensuring its recommendations align with diverse user needs. By merging intelligent healthcare with interactive digital support, NutriVision empowers users to make informed dietary choices, promoting healthier eating habits. This paper also presents the system’s design, performance evaluation, and potential real-world applications, highlighting its contributions to personalized nutrition.