<![CDATA[Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.]]>
