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A new method for estimating prediction intervals for a model output using machine learning is presented. In it, first the prediction intervals for in-sample data using clustering techniques to identify the distinguishable regions in input space with similar distributions of model errors are constructed. Then regression model is built for in-sample data using computed prediction intervals as targets, and, finally, this model is applied to estimate the prediction intervals for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction intervals. A new method for evaluating performance for estimating prediction intervals is proposed as well.