All major airport operators face a similar challenge, namely ensuring maximum throughput and maintaining high runway utilisation. A key part of this is accurately planning aircraft movements on the ground to avoid queueing and associated delays. A primary indicator of the operator performance in this area is the Taxi-Out Time. The research objective of this article is to review whether the application of machine learning can be used to model the departure process in such a way as to provide accurate prediction of TXOT taking into account a wide range of variables. A regression tree type machine learning model is developed using actual data from Vienna Airport and a selected set of significant predictor variables. The taxi-out times of the test set of flights are closely predicted with an RMSE of 2.03 minutes for normal taxi-out and 3.75 minutes for extended taxi-out.