The multistate effort to forecast epidemics of Fusarium head blight (FHB) in wheat and barley has made considerable progress in recent years. Collaborative work between the disease FHB Management Coordinated Project has expanded the dataset available for modeling to more than 1,200 cases. These observations incorporate information from additional production environments. This information has enabled the us to expand of the forecasting system for use in additional states, and account for advances in variety development. The continued effort to gather new observations also helps ensure the long-term stability of the forecasting models within changing climates. Recent advances in model development focused on building ensembles of predictive models based on Random Forests (RF) machine learning algorithms. This approach resulted in models that improved the overall prediction accuracy of the forecasts relative to previous generations of modeling. The RF modeling approach yielded multiple models with sensitivity and specificity greater than 80%. The RF models also provided useful insights into weather patterns that favor the development of FHB epidemics. For example, variables describing the stability of temperature prior to crop anthesis were the most commonly selected by the RF models. Consistent with previous modeling efforts, variables summarizing atmospheric moisture, such as relative humidity, dew point and vapor pressure deficit, were identified as critical predictors of FHB epidemics. There were also important advances with the web-based tools used to deploy the forecasting models including features that better display regional commentary of disease specialists along with the maps of disease risk. Features that enable users to visualize local weather conditions and trends in the disease risk will be available for the 2024 growing season. Recent user surveys of the forecasting system helped document the value of the information to small grain producers in the United States. These surveys indicated that 89% of the users thought that the information improved the profitability of their farm. Users reported that the forecasting system helped them avoid unnecessary fungicide applications and estimated that the value of the information exceeded $70 million annually.