With the increasing awareness of health and the popularization of competitive sports, the technological advancement and specialization of ball sports training have become a growing trend. In football training, the precise simulation of ball trajectories and the design of personalized training programs have emerged as key issues that need to be addressed urgently. To enhance the scientific rigor and effectiveness of football training and to promote its intelligent development, this study proposed an omnidirectional mobile intelligent ballistics trajectory simulation football training assistant robot by integrating technologies such as ball launching mechanisms, visual acquisition, data analysis, and motion control. Firstly, a forward dynamics model of football was constructed. Subsequently, considering complex physical factors such as air resistance and the Magnus force, this study designed an inverse kinematics solution model based on the RMSProp algorithm to solve the initial parameters for ball shooting, enabling precise adjustments of the yaw and pitch angles according to the target position, thereby achieving high-precision hits on the target point. Finally, a three-axis gimbal shooting robot capable of adjusting the shooting angle and position was developed and tested experimentally. Experimental results indicate that the training robot achieves a goal entry error of less than 0.45 m under various training conditions. The root mean square error between the theoretical and actual trajectories is less than 7.5 cm. These findings validate the robustness and precision of the previously described inverse kinematics solution model for ball launching. Additionally, this study established a detailed ball launching dataset, which can serve as an important resource for subsequent research in data science and artificial intelligence.