To solve the data imbalance problem and improve the predictive accuracy on warfarin daily dosage, we develop an evolutionary synthetic minority oversampling technique (ESMOTE), which is based on an evolutionary strategy (ES). ESMOTE oversamples the minority, whose genotypes are *1/*3 and *3/*3 for CYP2C9, AG and GG for VKORC1 or who took amiodarone and drank regularly. Ensemble learning method-Random Forest (RF) is used to build ensemble predictive model. RF produces a group of trees by training them on minority and the masses as well as different combinations of features. And, they make uses of correlation of tress to improve the generalization of predictive model. In the experiment, five machine learning methods are as comparators to ESMOTE-RF. These methods are tested on the inner dataset of The First Affiliated Hospital of Soochow University and an external dataset of International Warfarin Pharmacogenetics Consortium (IWPC). Results showed that ESMOTE-RF present the highest accuracy on the prediction of warfarin dose in terms of R-squared (R 2 ) and mean squared error (mse). In terms of the percentage of patients whose predicted dose of warfarin is within 20% of the actual stable therapeutic dose (20%-p), ESMOTE-RF can achieve satisfied prediction.