Limiting acute esophagitis remains a clinical challenge in the treatment of locally advanced non-small cell lung cancer using chemoradiotherapy. In this study, machine learning (ML) algorithms were used to predict acute esophagitis in patients (n = 451; training set 70% and independent test set 30%) treated in the Radiation Therapy Oncology Group 0617 clinical trial. Multiple ML models were trained to predict grade ≥ 2 esophagitis using clinical, radiomics, and dosimetric/dosiomics features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best-performing model was explained using the Shapley additive explanation framework and decision curve analysis. The highest-accuracy model was based on radiomics–dosiomics features using the least absolute shrinkage and selection operator model (AUC = 0.70; 95% confidence interval (CI): 0.60–0.78). A novel model that accounts for esophageal geometry relative to tumor location achieved an AUC of 0.67 (95% CI: 0.58–0.76) using dose–volume histogram relationships. The ML models revealed important relationships that can guide radiation dose planning to minimize individual risk and facilitate personalized radiation therapy.