Abstract: The advantages of artificial intelligence (AI) algorithms in rapid prediction, self-learning, and strong generalization have been applied to address the complex thermal-hydraulic phenomena and mechanisms in nuclear reactors. These applications include the prediction of thermal-hydraulic parameters, optimization of thermal safety analysis programs, and enhancement of computational fluid dynamics (CFD) efficiency. This paper reviews the current research status of AI algorithms in predicting thermal-hydraulic parameters such as flow regimes, boiling heat transfer, and critical flow. It proposes that AI models, such as physics-informed neural networks (PINNs), can overcome the challenge of insufficient extrapolation accuracy due to the lack of experimental data under high-parameter and specific structural conditions within reactors. The adaptive advantages of AI algorithms can address issues such as model singularity and convergence difficulties in safety analysis programs. Additionally, model calibration methods can significantly reduce the time and uncertainty involved in system modeling, while data assimilation techniques can minimize time-accumulated errors, greatly improving the accuracy of time-series data predictions. AI algorithms can also enhance the computational efficiency and accuracy of traditional CFD methods. Through model order reduction, they can effectively predict the three-dimensional thermal-hydraulic performance parameters of key nuclear reactor components.