Experimental results demonstrate that droplet vaporization on metal surfaces can be significantly enhanced with the application of a nanoporous, superhydrophilic surface coating. A thin layer of ZnO nanopillars can be easily seeded and grown on most metallic surfaces to achieve nanoscale pores between pillars, and ultra-low apparent contact angles. These surface coatings have the potential to improve spray cooling processes, and can be easily scaled up to larger and more complex heat exchangers. In order to characterize the potential improvement to a spray cooling system it is important to understand the dependence on system parameters, and to have a clear model of droplet vaporization on such surfaces. There are a number of surface and impact parameters that will affect the droplet spreading and subsequent vaporization on the surface. The surface contact angle, wicking speed and impact velocity all interact to affect the maximum spread of the droplet and the speed at which the droplet reaches this state. Along with variations in droplet volume and wall superheat, the model for droplet vaporization becomes more complex and nonlinear. Instead of exploring a single parameter at a time, machine learning tools can be utilized to determine the dependence of droplet evaporation time on these parameters simultaneously. In this study a genetic algorithm and a neural network were used to develop a droplet evaporation model for these superhydrophilic surfaces. Each algorithm demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.