We initially aimed to ascertain the application value of inflammatory indexes in predicting severe acute radiation pneumonitis (SARP). Furthermore, a novel nomogram and risk classification system integrating clinicopathologic, dosimetric, and biological parameters were built to provide individualized risk assessment and accurate prediction of SARP in patients with esophageal cancer who received radiation therapy. All data were retrospectively collected from 416 esophageal cancer patients in 2 participating institutes. A novel nomogram was constructed that forecasted SARP based on logistic regression analyses. The concordance index, calibration curves, and decision curve analyses were used by both internal and external validation to demonstrate discriminatory and predictive capacity. Moreover, a corresponding risk classification system was generated by recursive partitioning analysis. The Subjective Global Assessment score, pulmonary fibrosis score, planning target volume/total lung volume, mean lung dose, and ratio of change regarding systemic immune inflammation index at 4 weeks in the course of treatment were independent predictors of SARP and finally incorporated into our nomogram. The concordance index of nomogram for SARP prediction was 0.852, which showed superior discriminatory power (range, 0.604-0.712). Calibration curves indicated favorable consistency between the nomogram prediction and the actual outcomes. Decision curve analyses exhibited satisfactory clinical utility. A risk classification system was established to perfectly divide patients into 3 different risk groups, which were low-risk group (6.1%, score 0-158), intermediate-risk group (37.3%, score 159-280), and high-risk group (78.9%, score >280). The Subjective Global Assessment score, pulmonary fibrosis score, planning target volume/total lung volume, mean lung dose, and ratio of change regarding systemic immune inflammation index at 4 weeks were potential valuable markers in predicting SARP. The developed nomogram and corresponding risk classification system with superior prediction ability for SARP could assist in patient counseling and provide guidance when making treatment decisions.