Anthropometric indicators are commonly applied in health and nutritional assessments because they are simple, economical, and non-invasive. Among these indicators, Body Mass Index (BMI) is extensively used to evaluate obesity and nutritional status; however, it provides limited information regarding body fat distribution and composition. As a result, exclusive dependence on BMI may lead to misclassification in certain individuals. To address this limitation, recent research has emphasized the optimization of BMI prediction through the incorporation of multiple anthropometric measurements. This study examines the combined role of body weight, waist circumference, hip circumference, and neck circumference in enhancing BMI estimation. Advanced statistical optimization techniques, particularly Response Surface Methodology (RSM), provide a robust framework for analysing the simultaneous effects of these variables while capturing nonlinear relationships and interaction effects. The optimized models have potential applications in refining obesity classification, improving risk assessment, and supporting public health and clinical decision-making. This approach highlights the value of multivariate anthropometric optimization in overcoming the inherent limitations of traditional BMI-based evaluations...
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