Aims: This study aims to enhance the precision of obesity risk assessments by improving the accuracy of waist circumference predictions using machine learning techniques. Methods: We utilized data from the NHANES and Look AHEAD studies, applying machine learning algorithms augmented with uncertainty quantification. Our approach centered on conformal prediction techniques, which provide a methodological basis for generating prediction intervals that reflect uncertainty levels. This method allows for constructing intervals expected to contain the true waist circumference values with a high degree of probability. Results: The application of conformal predictions yielded high coverage rates, achieving 0.955 for men and 0.954 for women in the NHANES dataset. These rates surpassed the expected performance benchmarks and demonstrated robustness when applied to the Look AHEAD dataset, maintaining coverage rates of 0.951 for men and 0.952 for women. Traditional point prediction models did not show such high consistency or reliability. Conclusions: The findings support the integration of waist circumference into standard clinical practice for obesity-related risk assessments using machine learning approaches. Keywords: Biomarkers; Conformal prediction; Machine learning; Obesity risk assessment; Waist circumference.