This study investigates the effects of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develops machine learning models to predict SIRS. Analyzing data from 1,908 patients, researchers found SIRS significantly raised mortality risk. Key predictors included preoperative anemia and intraoperative lactate peaks. Predictive models using random forest achieved high accuracy, offering insights for tailored interventions.