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This study leveraged a large-scale dataset from NHANES 2013–2014 to gain insights into periodontitis pathogenesis and develop predictive tools. After cleaning and preprocessing the data, 15 crucial factors were identified from over 100 potential risk factors and utilized as input features for four machine learning algorithms: support vector machines (SVM), random forest (RF), neural network and XGBoost. The models were evaluated for periodontitis prediction performance through internal validation metrics such as specificity, accuracy, precision, recall and accuracy (area under the curve (AUC)). Notably, education level, household income and smoking status emerged as key risk factors, aligning with medical literature. While SVM and RFs excelled in specificity and accuracy, neural networks surpassed in precision and recall for periodontitis patients. XGBoost offered a balanced performance, making it a versatile choice. The feature importance analysis underscored the profound influence of socioeconomic factors and unhealthy lifestyle habits on periodontal health. This study contributes novel approaches and insights for periodontitis prevention and treatment, demonstrating clinical and societal significance. Future research should focus on optimizing and externally validating the model to enhance its generalizability and accuracy.