Forecasting flood extent during extreme events remains a critical challenge for hydrological modelling, particularly in data-scarce and highly dynamic floodplain systems. Accurate and timely forecasts of these events are essential for effective disaster preparedness and response. Traditional physically based methods are often not well-suited for modelling complex hydrodynamic systems, as they depend on fixed structural parameterisations of surface water processes, groundwater interactions, and evapotranspiration that are difficult to calibrate and scale in catchments with highly heterogeneous vegetation, climatology, and terrain. Machine learning approaches, which can learn nonlinear relationships directly from data without explicit physical parameterisation, offer a promising alternative for modelling flooding in these regions.</p> <p>We present INFLOW-AI v2.1, a machine learning framework for predicting extreme seasonal flood extent beyond what was observed in the training set. To enhance predictive accuracy for these out-of-sample extreme events, the framework employs a two-stage neural network architecture that combines (1) extreme-sensitive temporal thresholds with (2) dynamic spatial predictions. The first stage employs transformer-based models with multi-headed attention mechanisms to capture long– and short-term hydrometeorological patterns in total flood extent over the past 36 dekads. To enable more effective detection of extremes, this stage predicts the first difference of the seasonal anomaly in total flood extent, rather than the raw total flood extent. The second stage then dynamically models spatial flooding patterns using a ConvLSTM to predict local inundation probabilities at 1 km resolution, with the basin-scale inundation extent predicted by the first stage used to constrain the spatial predictions. The model generates forecasts with a lead time of up to six dekads (two months).</p> <p>A case study was conducted over the Sudd wetland in South Sudan, one of the world’s largest freshwater ecosystems which has experienced unprecedented catastrophic flooding beginning in June 2019, severely impacting Jonglei, Unity, and Upper Nile States. INFLOW-AI was tested on this catchment, demonstrating the two-stage model’s ability to predict extreme out-of-sample post-2019 flooding with only exposure to pre-2019 data. INFLOW-AI has been deployed operationally since the 2024 flood season (August– November) on the Joint Analysis System Meeting Infrastructure Needs (JASMIN), providing real-time predictions to humanitarian organisations and informing flood preparedness in South Sudan.