<p>This paper investigates the sliding mode controller design for a class of Markovian jump nonlinear systems, introducing a novel methodology based on fully actuated system approaches. A simple state-feedback controller is initially constructed by exploiting the system’s fully actuated characteristics. Neural networks are employed to approximate the unknown nonlinearities within the controller. This design is then enhanced by integrating sliding mode control to robustly compensate for both neural network approximation errors and external disturbances. The proposed fully actuated system approaches based sliding mode controller facilitates performance tuning via pole placement with fewer parameters than some existing methods. Finite-time boundedness of the closed-loop system is proven. The effectiveness and advantages of the proposed strategy are confirmed through numerical simulations.</p>