FinNutriAgent (FNA): An Agentic AI for Nutrition Planning Considering Budget Constraints

Toqeer Ali Syed; Abdulaziz Alshahrani; Ali Akarma; Sohail Khan; Muhammad Nauman; It Ee Lee; Salman Jan; Ali Ullah

doi:10.48084/etasr.15640

Authors

Toqeer Ali Syed Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia
Abdulaziz Alshahrani Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia
Ali Akarma Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia
Sohail Khan Department of Computer Science, Effat College of Engineering, Effat University, Saudi Arabia
Muhammad Nauman Department of Computer Science, Effat College of Engineering, Effat University, Saudi Arabia
It Ee Lee Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya, Malaysia | Centre for Smart Systems and Automation, COE for Robotics and Sensing Technologies, Multimedia University, Cyberjaya, Selangor, Malaysia
Salman Jan Arab Open University, Bahrain
Ali Ullah Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia

Volume: 16 | Issue: 3 | Pages: 36408-36417 | June 2026 | https://doi.org/10.48084/etasr.15640

Received: 19 October 2025 | Revised: 21 November 2025, 29 November 2025, and 21 December 2025 | Accepted: 24 December 2025 | Online: 14 May 2026

Corresponding author: Toqeer Ali Syed

Abstract

This paper presents an agentic Artificial Intelligence (AI) model that is price responsive and combines household budget constraints and dietary optimization. Based on income, fixed financial commitments, health-related requirements, and dynamically revised food expenses, the system produces meal plans that are nutritionally balanced, cost-effective, and respond automatically to market shifts. The proposed design is an implemented modular multi-agent system, which consists of specialized agents, budget planning, nutritional evaluation, price surveillance, and health-based personalization. These agents align themselves with a common body of knowledge and use food substitution graphs to maintain nutritional adequacy and to reduce spending. The assessment based on a representative household in Saudi Arabia shows 13-18% savings in food expenses in comparison to the fixed weekly menus, nutrient adequacy greater than 95%, and stability in response to simulated price shocks of ±20 - ±30%. These findings indicate that the framework is valuable to match the affordability with nutritional sufficiency and offers a scalable solution to robust household diet planning in line with the Sustainable Development Goals associated with Zero Hunger and Good Health.

Keywords:

agentic AI, household budgeting, diet optimization, nutritional adequacy, multi-agent systems, price-aware meal planning, sustainable development goals

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