This study investigates bone mineral density in 211 patients with systemic sclerosis and 505 age- and sex-matched controls. Clinical measurements, biomarkers and lifestyle information were analysed using regression models, alongside machine-learning approaches to predict total hip T-score. Elevated NT-proBNP and inflammatory markers were associated with lower bone density, while BMI and NT-proBNP were prominent predictors. Individual Shapley explanations illustrate how measured characteristics contribute to each patient’s predicted T-score. The work connects clinical research with interpretable prediction and highlights the need to consider disease-related factors alongside established bone-health risk factors. Independent validation is needed before these predictive models can support clinical decisions.