Thyroid cancer is the most common malignant tumor of the endocrine system, and its incidence has been steadily increasing in recent years. Papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC), collectively known as differentiated thyroid cancer (DTC), account for approximately 95% of thyroid cancer cases. Imaging examinations such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT/CT), and positron emission tomography (PET/CT) play a critical role in the diagnosis, staging, risk stratification, and treatment of DTC. However, the analysis of imaging results heavily depends on the skills and experience of the physician, making the interpretation prone to errors due to factors such as image volume, complexity, and subjective judgment, particularly under high workloads. Additionally, objective factors like equipment resolution and the limitations of human vision can also affect diagnostic accuracy. Artificial intelligence (AI), a technology that simulates, extends, and enhances human intelligence, has gradually been applied in the medical field. In the diagnosis and treatment of DTC, AI technologies such as machine learning and deep learning, as well as radiomics, have been utilized. These technologies are primarily applied in the diagnosis and differential diagnosis of DTC, staging assessments, prediction of genetic mutations, and iodine-131 therapy for DTC. The application of AI and radiomics holds great promise for improving diagnostic accuracy, enabling precise staging, predicting genetic mutations with higher precision, and optimizing treatment strategies for DTC. This advancement is expected to facilitate accurate diagnosis and personalized treatment, maximizing benefits for patients.