Mangrove ecosystems provide critical ecological services but face increasing pressure from anthropogenic activities and climate change. Accurate large-scale mapping is essential for effective conservation strategies. We produced a 2024 national mangrove map by merging Sentinel-2 multispectral imagery, Sentinel-1 synthetic-aperture radar (SAR) backscatter and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The analysis domain comprised all Global Mangrove Watch (2023) polygons with a 2 km buffer. From these layers we derived 23 predictors, including six spectral bands, six vegetation indices (e.g., Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Mangrove Vegetation Index, MVI), four radar texture metrics (VV, VH, VV/VH ratio, contrast) and terrain variables(elevation, slope, aspect). Five widely used machine-learning classifiers—Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Gradient Tree Boosting (GTB) and XGBoost—were combined through soft voting after grid-based hyper-parameter tuning. The ensemble achieved an overall accuracy of 97.0 \%, outperforming individual models (95.8–96.9 \%). Feature-importance analysis identified MVI as the strongest discriminator (0.209–0.720), followed by VV contrast (0.052–0.097) and elevation (0.044–0.089). The final map shows 2557 km2 of mangroves distributed across 24 provinces, with 75 \% located along the Andaman Sea coast. By blending complementary optical, radar and topographic information in a fully script-based Google Earth Engine (GEE) workflow, the study delivers an operationally scalable tool for national monitoring that supports conservation planning, carbon accounting and climate-adaptation policies.