<p>The increasing frequency and severity of wildfires, particularly in the wildland-urban interface, underscores the urgent need for advanced real-time wildfire forecast models. This study develops a cross-scale deep-learning model for high-resolution wildfire emergency management that uses wildfires in Hong Kong Island, China as a demonstration. We simulate massive wildfire scenarios with a high spatial resolution of 5 m, based on historical fire records, and establish a numerical dataset of 240 fire cases (8640 samples of burnt area developing from a spot to vast landscape). Then, we introduce a cross-scale framework to achieve high-resolution wildfire spread forecast by avoiding the high-cost direct deep learning of high-resolution images. The framework forecasts the small-scale fire with 5-m resolution in the first 12 h and then smoothly transitions to 40-m resolution for forecasting the large-scale fire. The model is demonstrated to forecast the wildfire front and burning region crossing the spatial scale from 25 m² to 20 km² and achieve an overall accuracy of above 75% with a lead time ranging from 2h to 72 h. Finally, we develop a practical software, Intelligent Wildfire Forecast Tool (IWFTool), to integrate the cross-scale AI framework for supporting wildfire emergency response. The proposed smart framework enables the application of accurate, low-cost and fast-training AI tools for high-resolution wildfire forecasts and emergency responses.</p>