Tan et al. analyzed MIMIC-IV data (2008–2019) to identify clinical predictors of hypothermia in patients with septic shock, aiming to improve early risk stratification in critical care. Among 1,640 ICU patients with septic shock, 134 (8.2%) developed hypothermia, highlighting a clinically significant high-risk subgroup. Univariate logistic regression was used to identify key risk factors associated with hypothermia in septic shock patients, guiding subsequent predictive modeling. Five independent predictors: hemoglobin, heart rate, respiratory rate, lactate, and dopamine use, were strongly associated with hypothermia in septic shock patients. These variables were incorporated into a nomogram predicting hypothermia risk, with an AUC of 0.787, demonstrating robust internal validation and potential bedside applicability. This model reliably stratifies septic shock patients by hypothermia risk, supporting timely interventions in the ICU.