Exploring parametric effects in pool boiling is challenging because the dependence of the resulting surface heat flux is often nonlinear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have been deduced by fitting relations obtained from physical models to experimental data and from correlated trends in nondimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the system physics. The investigation summarized here explores the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how a genetic algorithm and deep learning can be used to extract heat flux dependencies of a binary mixture on wall superheat, gravity, Marangoni effects, and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for nucleate boiling. Each technique demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.