The main distributing valve (MDV) plays a core role in hydraulic amplification and precise control within the hydro-turbine governing system, and its spool position dynamics directly affect the regulation quality and stability of the hydro-turbine generator unit. However, developing a high-fidelity dynamic model of the MDV remains challenging, as existing methods typically rely on linearization assumptions and fail to fully account for system uncertainties, resulting in limited accuracy and poor cross-condition stability. To address this issue, this paper proposes a mechanistic–data-driven hybrid modeling method. Firstly, a modular modeling strategy is adopted to accurately construct a nonlinear mechanism-based model (MBM) that conforms to the actual industrial system. Secondly, to address the unknown dynamics not captured by the MBM, a Bayesian optimization-enhanced Light Gradient Boosting Machine (BO-LightGBM) model is developed. Then, a cascade–additive architecture is employed to achieve effective integration of the two. Finally, verification experiments are conducted under various dynamic processes based on the actual operating data of the governor. The results show that the proposed hybrid model significantly outperforms the other five comparison models in terms of accuracy and stability while retaining physical interpretability. This provides a more practically valuable solution for the dynamic modeling and simulation of hydroturbine actuators.