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AI-Driven Personalized Meal Planning: A Web-based Platform for Tailored Nutrition and Health Management
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 57 |
| Year of Publication: 2025 |
| Authors: Shahid Hussain, Nazeem Khan, Syeda Amna Ali Shah, Shareefa Bano, Abdul Wadood Khan |
10.5120/ijca2025925913
|
Shahid Hussain, Nazeem Khan, Syeda Amna Ali Shah, Shareefa Bano, Abdul Wadood Khan . AI-Driven Personalized Meal Planning: A Web-based Platform for Tailored Nutrition and Health Management. International Journal of Computer Applications. 187, 57 ( Nov 2025), 17-29. DOI=10.5120/ijca2025925913
Abstract
Modern personalized nutrition has raised a great deal of interest with respect to individual health and disease prevention. Conventional menus commonly offer generic guidelines that do not take personal health, preferences, and cultural concerns into consideration. In this paper, we introduce the Personalized AI Meal Planner, which is a platform that uses AI algorithms to deliver meal plans tailored to each user’s health data and dietary likes and dislikes. The platform employs DeepSeek-R1 for processing health data and ChatGPT for meal recommendation generation tailored to maximize health outcomes, including weight loss, blood sugar control, and cholesterol management. It provides things like suggestions for recipes at the tap of a button, an organized grocery list, and full-on, to-the-letter instructions facilitating both meal planning and cooking. Positive user engagement was encouraged by the system, which adjusts to real-time feedback about users and their health, so that meal plans remain consistent with individual goals around healthy eating. These findings provided evidence that the system worked consistently to help users improve their BMI, blood sugar, and cholesterol. Compared with available meal planning systems, including Meta Nutrition Meal Planner and My Diet Meal Plan, the Personalized AI Meal Planner showed a better performance in personalization and health outcomes by delivering more personalized meal plans that can be adapted according to users’ preferences. Yet, the quality of recommendations depends on the accuracy of user data input, which can influence system performance. Future work will involve the inclusion of wearable health device data and will extend the system database to be inclusive of a broader set of cultural cuisines and dietary restrictions, for greater flexibility in usage by its users. Overall, the analysis reveals that AI-enabled meal planners have significant opportunities in precision nutrition, providing a comprehensive pipeline for orchestrating lifelong health and well-being.
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