Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing accumulation of software errors with time causes a phenomenon called software aging. This phenomenon can result in system performance degradation and eventually system hang/crash. To cope with software aging, software rejuvenation has been proposed.Software rejuvenation is a proactive technique which leads to removing the accumulated software errors by stopping the system,cleaning up its internal state, and resuming its normal operation.One of the main challenges of software rejuvenation is accurately predicting the time to crash due to aging factors such as memory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash under different aging scenarios. Finally, by comparing the accuracy of different techniques, it can be concluded that the multilayer perceptron neural network has the highest prediction accuracy among all techniques studied. A growing accumulation of software errors with time causes a phenomenon called software aging. This phenomenon can result in system performance degradation and eventually system hang / crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to removing the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main challenges of software rejuvenation is accurately predicting the time to crash due to aging factors such as memory leaks. In this paper, different machine learning techniques are compared to precisely predict the software time to crash under different aging scenarios. said that the multilayer perceptron neural network has the highest prediction accura cy among all techniques studied.