Fuzzy logic provides a robust framework for addressing the uncertainties and complexities inherent in nutritional science. Traditional approaches to dietary guidelines often fail to capture the nuanced variability among individuals, such as metabolic differences, preferences, and specific health conditions. This paper explores the application of fuzzy logic in nutrition, emphasizing its potential to personalize dietary recommendations, optimize nutrient intake, and improve decision-making in clinical and public health settings. Through case studies and simulations, we illustrate the advantages of fuzzy logic over conventional methods, highlighting its ability to handle imprecision and ambiguity in nutritional data.