A machine learning framework is developed to estimate ocean-wave conditions. By supervised training of machine learning models on many thousands of iterations of a physics-based wave model, accurate representations of significant wave heights and period can be used to predict ocean conditions. A model of Monterey Bay was used as the example test site; it was forced by measured wave conditions, ocean-current nowcasts, and reported winds. These input data along with model outputs of spatially variable wave heights and characteristic period were aggregated into supervised learning training and test data sets, which were supplied to machine learning models. These machine learning models replicated wave heights from the physics-based model with a root-mean-squared error of 9 cm and correctly identify over 90% of the characteristic periods for the test-data sets. Impressively, transforming model inputs to outputs through matrix operations requires only a fraction (<1/1,000th) of the computation time compared to forecasting with the physics-based model. •Machine learning models were trained to act as a surrogate for the physics-based SWAN model.•An MLP model accurately represented the significant wave height field and an SVM model simulated the characteristic period.•Machine learning models represented the SWAN-simulated wave conditions in less than 1/1000th of the computational time.