Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study aimed to develop a personalized, supervised machine learning-driven predictive model for multiple chronic toxicities by integrating clinical, dosimetric, and genetic data specifically evaluating the impact of the NBN gene rs1805794 (c.553G>C) polymorphism. Methods: This study enrolled 125 patients with HNSCC who received curative-intent radiotherapy and remained disease-free during follow-up with a median of 98 months. Comprehensive clinical and dosimetric data were collected, and chronic toxicities were recorded. Peripheral blood samples were analyzed for the NBN rs1805794 polymorphism using allele-specific PCR (AS-PCR). Following feature selection, four supervised machine learning classifiers were trained and evaluated to identify the optimal model for predicting multiple chronic toxicities. Results: The XGBoost algorithm emerged as the highest performing model. Baseline clinico-dosimetric predictors of multiple chronic toxicities included PTV70 volume, the addition of concurrent chemotherapy, advanced T and N stages, and continued smoking. Integrating the NBN rs1805794 genotype into the XGBoost architecture enhances its predictive capability. The final model accurately identified patients at high risk for multiple chronic toxicities, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, a sensitivity of 0.74, and a specificity of 0.79. Conclusions: Integrating clinical, dosimetric, and genetic data within a machine learning framework effectively predicts multiple chronic toxicities in HNSCC. This approach enabled early risk stratification, providing the potential for personalized therapy.