The control of liquid sloshing in moving containers plays a crucial role, directly affecting the stability of the entire system, especially for liquid-carrying structures in the aerospace, space, and chemical industries. This paper introduces an adaptive sliding mode control strategy, which combines with a Radial Basis Function neural network (RBF-SMC) to improve the efficiency of liquid-filled container transportation systems. Starting with the dynamic model of the sloshing system by using an equivalent single pendulum model, considering uncertainties, an adaptive robust controller based on RBF neural networks is applied to estimate the uncertain components in the model. The simulation results consistently show that the adaptive controller enables the output signal to rapidly track the reference signal, even when the model parameters are uncertain. This study establishes an important foundation for enhancing vibration suppression in practical sloshing systems.