This work presents an innovative methodology for identifying instances of honey adulteration by utilizing the Vision Transformer (ViT) model and thermal imaging techniques to assess and classify honey samples. Conventional techniques employed for the identification of honey adulteration are characterized by protracted processing durations and frequently exhibit limited sensitivity. Thermal imaging is a distinctive benefit as it enables the identification of temperature fluctuations within honey samples, hence facilitating the assessment of disparities in sugar composition, moisture levels, and the existence of adulterants. Thermal imaging technique offers a notable advantage in the detection of adulterants, as it may reveal temperature variations within honey samples caused by differences in sugar composition, moisture levels, and other adulterating substances. To establish a dependable method for classifying honey, we gathered an extensive dataset comprising thermal pictures of 9 unadulterated honey samples, as well as 84 honey samples that were contaminated at varying levels ranging from 1% to 30% throughout their chilling procedures. The data set was also employed to fine-tune and train the model developed in this study. The findings showed that the model achieved an accuracy rate of 99.9%, alongside a sensitivity of 99.5% and specificity of 100% and provide evidence of the efficiency of thermal image analysis by Transformers as an effective method for identifying instances of honey adulteration rapidly and accurately. The above-discussed approach provides an apparently viable solution to enforcing quality control policies within the honey trade that will ensure the safety and authenticity of this valuable organic product.