One of the main challenges facing the banks is to determine the proper bank liquidity. Risk differs widely from bank to bank, and a Careful understanding of various risk factors assists predict the likelihood of expected liquidity based on historical data, Real-world datasets often have missing values, which can cause bias in results. the most widely adopted method for dealing with missing data is to delete observations having missing values, these methods have the disadvantages represented in loss of precision and biased. The purpose of this study is to forecast banks' liquidity risk. We also present a method for dealing with missing data using powerful machine learning methods. we Used available datasets through Kaggle there are 350 cases and 19 characteristics in this dataset. SPSS and the WEKA tool were used to analyze the data. ROC and accuracy were used to assess and compare three classification models (Decision Tree, Support Vector Machine (SVM), and random forest ). Results showed that the model obtained acceptably, results The 66-fold( 97.47, 97.47, 97.47) respectively (DT, SVM, RF) the best accuracy among from 10-fold.