Machining production relies heavily on the quality of the surface roughness, and researchers have spent years studying ways to predict it. Typically, three approaches are taken to model surface roughness: empirical methods, theoretical/simulation methods, and soft computing methods. This study involved using Machine Learning models to analyze two datasets generated from machining experiments. These experiments involved turning AISI H13 steel with cutting fluid. The first dataset, which contained 324 samples, was based on theoretically new-tool conditions. The second dataset, which contained 288 samples, varied cutting tool flank wear in three levels. To increase the available data, a strategy was employed to boost by six times the number of measurements without increasing the number of experiments.