While many studies focus on the persistence of coastal wetlands under climate change, similar predictions are lacking for new wetland establishment, despite being critical to restoration. Recent experiments revealed that marsh seedling establishment is driven by a balance between physical disturbance of bed-level dynamics and seedling root stability. Using machine learning, we quantitatively translate such finding in a new biogeomorphic model to assess marsh establishment extent. This model was validated against multiyear observations of natural seedling-expansion events at typical sites in the Netherlands and China. Subsequently, synthetic modeling experiments underscored that seedling expansion was primarily determined by controllable local conditions (e.g., sediment supply, local wave height, and tidal flat bathymetry) rather than uncontrollable climate change factors (e.g., change in sea-level and global wave regime). Thus, science-based local management measures can facilitate coastal wetland restoration, despite global climate change, shedding hope for managing a variety of coastal ecosystems under similar stresses.