<p>Artificial Intelligence (AI) is revolutionizing the approach to thyroid cancer treatment by improving both diagnostic precision and therapeutic methods. This review presents an in-depth analysis of AI-enhanced techniques, such as machine learning, deep learning, and radiomics, applied to the diagnosis and management of thyroid cancer. AI using the deep learning model exhibits greater sensitivity and specificity when compared to traditional diagnostic techniques and radiologists with varying levels of expertise. This review explores various learning paradigms, including supervised, unsupervised, and ensemble learning, highlighting AI’s supportive role in collaboration with medical professionals like dosimetrists and physicists. This piece also revealed that AI contributes to more accurate diagnoses, refines treatment planning, and assists in creating individualized therapeutic approaches. Our result proved that in diagnostic performance, AI achieved 90.6% and 91.0% in specificity and sensitivity, respectively in recent years. It also shows results for an experienced radiographer and the AI in terms of specificity, sensitivity, and accuracy were 86%, 80%, and 84% for the radiographer and that of AI was 85%, 80%, and 84%. It also finds that AI enhances the interpretation of medical images, streamlines workflows, and bolsters clinical decision-making, ultimately leading to improved patient outcomes, less invasive testing, reduced anxiety, quicker decision-making, and saving time and resources. Despite its potential, this work has identified and outlined how the integration of AI into clinical practice is hindered by challenges such as limitations in data, difficulties in model interpretability, and regulatory issues. We hereby suggest that overcoming these challenges is essential for the successful incorporation of AI into clinical settings, ensuring its place as a pivotal asset in precision oncology and the advancement of thyroid cancer care.</p>