We develop two national-level RFR models to predict AADT for MDVs and HDVs. These models incorporate total AADT, FHWA roadway functional classification, number of through lanes, and indicator variables for state and county. Given the dataset's scale and the computational demands of training each model, we use Bayes Search Cross Validation for hyperparameter tuning. This approach optimizes each model based on negative root mean squared error over 48 iterations with 3 cross-validation folds. For the MDV AADT model, we determine that 98 estimators with no maximum depth provide optimal performance. Conversely, the HDV AADT model benefits from 95 estimators with a maximum depth limited to 40. Both models are configured with a minimum sample split of 2, a minimum sample leaf of 1, and no maximum features.