In the present study, an infrared (IR) thermal camera was used to map the temperature of the target skin surface, and the resulting thermal image was evaluated for the presence of carotid artery stenosis (CAS). In the presence of stenosis in the carotid artery, abnormal temperature maps are expected to occur on the external skin surface, which could be captured and quantified using IR thermography. A Duplex Ultrasound (DUS) examination was used to establish the ground truth. In each patient, the background-subtracted thermal image, referred to as full thermal image, was used to extract novel parametric cold thermal feature images. From these images, statistical features, viz., correlation, energy, homogeneity, contrast, entropy, mean, standard deviation (SD), skewness, and kurtosis, were calculated and the two groups of patients (control and diseased: a total of 80 carotid artery samples) were classified. Both cut-off value- and support vector machine (SVM)-based binary classification models were tested. While the cut-off value classification model resulted in a moderate performance (70% accurate), SVM was found to have classified the patients with high accuracy (92% or higher). This preliminary study suggests the potential of IR thermography as a possible screening tool for CAS patients.