The hydrophobicity of functionalized interfaces can be quantified by the structure and dynamics of water molecules using molecular dynamics (MD) simulations, but existing methods to quantify interfacial hydrophobicity are computationally expensive. In this work, we develop a new machine learning approach that leverages convolutional neural networks (CNNs) to predict the hydration free energy (HFE) as a measure of interfacial hydrophobicity based on water positions sampled from MD simulations. We construct a set of idealized self-assembled monolayers (SAMs) with varying surface polarities and calculate their HFEs using indirect umbrella sampling calculations (INDUS). Using the INDUS-calculated HFEs as labels and physically informed representations of interfacial water density from MD simulations as input, we train and evaluate a series of neural networks to predict SAM HFEs. By systematically varying model hyperparameters, we demonstrate that a 3D CNN trained to analyze both spatial and temporal correlations between interfacial water molecule positions leads to HFE predictions that require an order of magnitude less MD simulation time than INDUS. We showcase the power of this model to explore a large design space by predicting HFEs for a set of 71 chemically heterogeneous SAMs with varying patterns and mole fractions.