The work aims to save time in predicting the accuracy of E-jet printing, solve the problems in selection of electrofluidic process parameters, and improve the design quality and efficiency of electrofluidic printing. A combination of finite element models and machine learning was proposed to predict the accuracy of E-jet printing. Based on machine learning algorithms such as linear regression, support vector regression and neural networks, a model on relationship between four parameters and jet diameter was established. The algorithm results showed that the determination coefficient R2 of the support vector regression and neural network prediction models could reach above 0.9, indicating that the models were highly credible; RMSE and MAE, which were indicators of model error, were both smaller than those of the linear regression prediction models. Machine learning algorithms enable effective prediction of E-jet printing accuracy, increasing prediction efficiency by more than a factor of ten and saving time on accuracy prediction.