Wildfires are among the most common and widespread natural hazards that impact landscapes and communities throughout the Western United States. Beyond their immediate effects, wildfires cause vegetation loss that can make watersheds susceptible to postfire debris flows and flash floods for several years, posing risks to downstream communities, properties, and vital infrastructure. Existing early-warning tools for postfire debris flows address specific contexts, such as inland and dry coastal regions, but they are not yet well-adapted to cooler, wetter areas like the coastal Pacific Northwest. This project integrates Artificial Intelligence (AI) and process-based earth surface response models into a Framework of AI-enhanced Modeling of Wildfire Geohazards (FAIM-WG). This framework will enable the identification of rainfall thresholds for triggering debris flows, explore new and missing processes to improve predictions, and develop transferable models for postfire debris flows to aid early-warning and risk mitigation efforts. This project will first create a comprehensive AI-ready, multi-modal dataset that includes topographic, meteorological, and environmental variables for all major wildfires across the Western U.S. This dataset will serve as the foundation for developing probabilistic models to predict postfire debris flow initiation using machine learning methods such as gradient-boosted decision trees, taking into account the unique characteristics of different regions