Thyroid cancer is a prevalent type of endocrine carcinoma that develops within the thyroid gland. Considerable resources have been dedicated to enhancing its diagnosis, with thyroidectomy serving as the primary treatment approach. The effectiveness of surgery, while minimizing unnecessary damage, hinges on an exact preoperative diagnosis. However, human evaluation of the malignancy of thyroid nodules is susceptible to inaccuracies and does not always ensure precise preoperative diagnoses. This study investigates the application statistical analysis, and data mining techniques to enhance thyroid cancer detection. Building upon previous research, our findings demonstrate the promising potential of these computational methods in improving diagnostic accuracy. Despite slight variations in performance metrics, both our study and previous work consistently show superior performance compared to expert assessments alone, with an accuracy of 83.4% surpassing the previous study’s 82%.