International Journal of Scientific Research and Engineering Development

In the contemporary era, the rapid shift towards sedentary lifestyles has significantly increased the prevalence of metabolic disorders and lifestyle diseases. While public awareness regarding nutrition has grown, individuals frequently struggle to formulate biologically appropriate meal plans due to the vast, often contradictory, dietary information available online. Traditional consultations with dieticians can be financially and logistically inaccessible for the general population. To bridge this gap, this research proposes the design and implementation of an "AI-Based Diet Recommendation System" a full-stack web application engineered to autonomously generate personalized nutritional plans. Utilizing a robust architecture composed of React.js, Node.js, and a relational database, the system acts as an expert computational engine. It ingests anthropometric inputs—namely age, gender, height, weight, activity levels, and dietary preferences—to calculate precise physiological metrics. By leveraging the Mifflin-St Jeor equation for Basal Metabolic Rate (BMR) and adjusting for Total Daily Energy Expenditure (TDEE), the algorithmic core deduces the exact caloric target required to achieve the user's specific health objectives (weight loss, maintenance, or weight gain). The system then employs a dynamic filtering algorithm to query a comprehensive meal database, mathematically distributing calories across breakfast, lunch, dinner, and snacks. This paper comprehensively details the system architecture, mathematical models, and algorithmic workflows utilized to democratize access to personalized, evidence-based nutritional planning.

Ms.Sanjivani Kole, Ms.Shweta Nagshetti, Ms.Shreya Swami, Ms.Shilavanti Daragonda, Ms.Harshda Bhandare, Mr.Aakash Chatake,"Design and Implementation of an Intelligent Web-Based Diet Recommendation System Using Anthropometric Data Analysis" International Journal of Scientific Research and Engineering Development, V9(2): Page(920-924) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.