The aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this pur- pose, we consider a database from the United Network for Organ Shar- ing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of Amer- ica from November 2004 until June 2015. The dataset contains more than a 74% of censoring, being a challenging and difficult problem. Sev- eral methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor char- acteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation met- rics are used, being the concordance index (ipcw) the most suitable for this problem. The results achieved show that, for each measure, a dif- ferent technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.