Biomarkers are quantifiable biological indicators that reflect an individual’s health status. These measurements may involve a single variable or a combination of variables that collectively provide insights into physiological conditions. Biomarkers serve as early warning signals for potential health issues, enabling timely interventions at both individual and population levels. Nutritional biomarkers, in particular, assess the biological impact of dietary intake. In our previous work, we employed machine learning techniques to predict weight, metabolic syndrome, and blood pressure using blood-exam-based biomarkers. In this study, we leverage extreme value theory to assess the significance of outliers in health data, with a particular emphasis on dietary factors and standard biochemical profiles. Specifically, we demonstrate that extreme value analysis, combined with a systematic approach, can facilitate the prediction of health trends and partially automate health interventions. To validate our approach, we utilized publicly available datasets from the National Health and Nutrition Examination Survey (NHANES), a comprehensive program that evaluates the health and nutritional status of the U.S. population. Our analysis encompassed approximately 70,000 data points spanning a decade of observations, providing a robust basis for our findings.