Orofacial pain is a complicated condition that frequently presents significant diagnostic challenges as a result of its complex nature and overlapping clinical presentations. In order to prevent protracted discomfort and ineffective treatment, it is essential to achieve early and accurate detection. This study suggests a non-invasive diagnostic method that combines infrared thermal imaging with machine learning techniques to differentiate between normal and abnormal cases of orofacial pain. Thermal facial images were acquired under standardized conditions, pre-processed, and subjected to a comprehensive feature extraction process that included statistical, textural, spatial, and raw pixel properties. These were standardized, dimensionality-reduced using Principal Component Analysis (PCA), and classified using Decision Tree, XGBoost, Random Forest, and Naive Bayes algorithms. In order to promote a comprehensive assessment of the accuracy, precision, recall, F1 score, and Area under receiver operating characteristics (AUC) metrics, a stratified 5-fold cross-validation was implemented. The Decision Tree exhibited the highest accuracy among the other models, demonstrating its capacity to accurately identify all true positive cases without missed diagnoses while maintaining high precision. The PCA projection demonstrated a distinct distinction between normal and aberrant cases, with classifiers that are capable of navigating non-linear boundaries. The results indicate that the integration of interpretable machine learning models with thermal imaging can yield a clinically viable, objective, and dependable tool for the detection of orofacial pain. This tool has the potential to be integrated into clinical decision support systems.