Increased awareness of health and longevity, coupled with the rapid growth of digital health information, has created a demand for intelligent tools that are capable of taking raw data and turning it into individual, actionable recommendations. This thesis outlines the design and construction of a rule-based nutrition and lifestyle consultancy recommendation system. The architecture is decoupled, separating domain knowledge captured in logical form in JSON from the Python-written analytical center. This offers flexibility, transparency, and maintainability, enabling health professionals to vary recommendations without modifying the source code of the system. The backend, developed with FastAPI, talks to Firebase, the central repository for user details and authentication, in real time.