In this paper, a finite-time Q-Learning algorithm is designed and applied to the mountain car problem. Compared with traditional Q-Learning algorithms, our designed finite-time Q-Learning algorithm can achieve learning objectives more rapidly. It is widely recognized that the operational efficiency of the Q-Learning algorithm heavily depends on the capabilities of the underlying hardware, and computational processes often consume a considerable amount of time. To reduce the time overhead associated with Q-Learning execution, this study utilizes the theoretical framework of finite-time stability to devise a novel Q-Learning algorithm. This innovative approach has been effectively implemented to tackle challenges related to the mountain car problem. Simulation results show a significant reduction in training completion time, along with a substantial increase in the subsequent success rate of the algorithm's performance.