Mangrove ecosystems play a crucial role in climate change mitigation through their exceptional capacity for carbon storage, yet their distribution in Indonesia—the country with the second-longest coastline in the world—remains underutilized for large-scale restoration and management. Despite being home to around 25% of the world’s mangrove forests, many coastal areas have lost their mangrove cover due to massive degradation and conversion. This study aims to map mangrove habitat suitability across Indonesia and to provide a predictive framework for restoration planning. This study integrated multi-source remote sensing and socio-environmental datasets—including temperature, precipitation, elevation, slope, tidal, land cover, nighttime light, and population density—into a Random Forest classification model, using Global Mangrove Watch 2020 data as the source of training labels. Model performance was evaluated using confusion matrixbased metrics. The Random Forest model achieved a robust result of classification, with an overall accuracy and a kappa coefficient of 97.81% and 0.96, respectively. The model identified approximately 10.09 million hectares of coastal zones as suitable for mangrove growth, a spatial extent three times larger than the existing mangrove cover. These results emphasize the opportunity in Indonesia to scale up mangrove restoration, contribute to national climate targets, and enhance coastal resilience.