Rapid assessment of coastal inundation along the Western Black Sea coast requires computationally efficient methods that can screen multiple compound storm scenarios while preserving a clear link to the controlling physical drivers. This study develops and evaluates an XBeach-informed machine-learning surrogate for coastal inundation and threshold-based flood-extent screening. The framework combines high-frequency sea-level observations from the Maritime Hydrographic Directorate monitoring network, Copernicus Marine wave and Black Sea physical products, ERA5 atmospheric forcing, Global Runoff Data Centre Danube discharge, bathymetric and coastal-elevation descriptors, and an intermediate cross-shore physical-response layer. The modelling dataset consists of 5,000 Monte Carlo forcing scenarios, 22 forcing and derived predictors, and a 10 × 10 spatial target grids, corresponding to 100 inundation-depth nodes per scenario. Four tree-based residual models, namely Random Forest, Gradient Boosting, Extra Trees, and Histogram Gradient Boosting, are combined using validation-derived weights.</p> <p>The conservative held-out node-level evaluation shows moderate quantitative skill for continuous inundation-depth prediction. The ensemble reaches R² = 0.409, RMSE = 1.181 m, MAE = 0.678 m, NSE = 0.409, KGE = 0.235, Pearson r = 0.914, and Spearman ρ = 0.923. In contrast, threshold-based flood-extent classification above the 0.30 m operational inundation threshold is substantially stronger, with F1 = 0.995, AUC-ROC = 0.9998, Matthews correlation coefficient = 0.989, and Cohen’s κ = 0.989. Split conformal prediction intervals provide near-nominal 90 % marginal coverage, with empirical coverage of 0.911, but the mean 90 % interval width is large, 3.25 m, indicating limited sharpness for local quantitative depth estimation. Extreme-event diagnostics show systematic underprediction of upper-tail inundation depths, with a mean bias of approximately −2.59 m for events above the 90th percentile.</p> <p>The present configuration is therefore most appropriate for rapid flood-extent screening, scenario ranking, and identification of cases where more detailed process-based simulations are required. It should not yet be interpreted as a stand-alone operational predictor of maximum inundation depth. The study demonstrates how in situ observations, Copernicus Marine products, physical-response modelling, machine-learning emulation, and uncertainty diagnostics can be combined into a transparent coastal-hazard screening workflow for the Western Black Sea coast.