Virtual lifetime estimation of microelectronic devices has become essential, due to the tightening requirements and the trend of electronification. Thermal cycling load cause fatigue crack in solder joints and therefore functional failures. The application of the fatigue models viable in the literature is circumstantial for industrial scale problems. The exploitation of the already existing simulation data in data-driven models is a promising solution for speeding up the virtual lifetime estimation process. This paper benchmarks data-driven methods to predict the lifetime of solder joints with different geometric properties using a dataset originated from finite element simulation results. This case study shows that the nonlinear function fitting and the neural network are applicable.