Forecasting banking system liquidity is crucial for the eective monetary policy implementation. This study investigates the eectiveness of various econometric and machine learning models in predicting the autonomous factors of banking system liquidity. The research compares widely used econometric models such as SARIMA, Exponential Smoothing, and Prophet alongside ma- chine learning models like Random Forest, applying various preprocessing techniques, including power transformations, scaling, and trend-cycle decomposition. Moreover, ensemble methods, like weighted blending and stacking, were used to improve accuracy. Experimental results in- dicate that SARIMA was the best individual model, but ensemble with Prophet and Random Forest further improved forecast performance. Neural network models underperformed poten- tially due to challenges in optimizing their architectures. Future research intends to explore multivariate and structural models, as well as advanced neural architectures, to enhance pre- dictive accuracy.