Abstract: Low-temperature plasma (LTP) is a form of plasma that is widely used in many industrial fields. Numerical simulation is an important means of studying and analyzing LTP. In recent years, with the advancement of artificial intelligence (AI) technology, AI-driven numerical simulation methods have gradually been applied in the field of LTP, which is expected to overcome the shortcomings of traditional numerical simulation methods. This paper focuses on LTP and first introduces mainstream LTP simulation models, including kinetic models, fluid models, chemical dynamics models, and hybrid models. We analyzed the problems faced by traditional LTP numerical simulation methods from four aspects: model complexity, numerical computation, parameter consistency, and result reliability. Then, using data-driven methods, physics-informed data-driven methods, and numerical simulation acceleration strategies as classification criteria, we introduced and analyzed the current research status of AI-driven LTP numerical simulation in detail. Finally, from the perspective of convergence and generalization, we summarized the challenges faced by relevant research and proposes further development directions.