Summary: In statistics, time-to-event analysis methods traditionally focus on the estimation of hazards. In recent years, machine learning methods have been proposed to directly predict the event times. A method based on vine copula models is proposed to make point and interval predictions for a right-censored response variable given mixed discrete-continuous explanatory variables. Extensive experiments on simulated and real datasets show that the proposed vine copula approach provides a decent approximation to other time-to-event analysis models including proportional hazards and Weibull Accelerate Failure Time models. When the proportional hazards or Weibull Accelerate Failure Time assumptions do not hold, predictions based on vine copulas can significantly outperform other models, depending on the shape of the conditional quantile functions. This shows the flexibility of the proposed vine copula approach for general time-to-event datasets.