By applying unsupervised machine learning techniques, this cross-sectional study provides a novel approach to enhance the clinical interpretation of body composition in newly diagnosed patients with gastrointestinal cancers, a population at high risk of malnutrition. To investigate the clinical relevance of this approach, we evaluated the association between the identified body composition profiles, nutritional risk, and health-related quality of life. Our findings indicate that male patients with a low muscle profile had a threefold higher risk of malnutrition. Nevertheless, malnutrition risk emerged as the only determinant of poorer quality of life, underscoring the importance of monitoring nutritional status throughout the cancer continuum. In this context, integrating body composition profiles into the assessment of gastrointestinal cancer patients may improve nutritional risk stratification and support the implementation of early, tailored, multimodal interventions. Further studies are warranted to evaluate longitudinal changes in body composition profiles and their association with clinically relevant outcomes.