Sea surface slope (SSS) responds to oceanic processes and other environmental parameters. This study aims to identify the parameters that influence SSS variability. We use SSS calculated from multi-year satellite altimeter observations and focus on small resolvable scales in the 30-100 km wavelength band. First, we revisit the correlation of mesoscale ocean variability with seafloor roughness as a function of depth, proposed by Gille et al. (2000). Our results confirm that in shallow water, there is statistically significant positive correlation between rough bathymetry and surface variability, while the opposite is true in the deep ocean. In the next step, we assemble 27 features as input variables to fit the SSS with a linear regression model and a boosted trees regression model, then make predictions. Model performance metrics for the linear regression model are R2 = 38.1% and mean squared error = 0.010 μrad2. For the boosted trees model, R2 = 56.3%, and mean squared error = 0.007 μrad2. Using the hold-out data, we identify the most important influencing factors to be the distance to the nearest thermocline boundary, significant wave height, mean dynamic topography gradient, and M2 tidal speed. However, there are individual regions, i.e. the Amazon outflow, that cannot be predicted by our model, suggesting that these regions are governed by processes not represented in our input features. The results highlight both the value of machine learning and its shortcomings in identifying mechanisms governing oceanic phenomena.