Wildfires are becoming unpredictable natural hazards in many regions due to climate change.However, existing state-of-the-art wildfire forecasting tools, such as the Fire Weather Index (FWI), rely solely on meteorological input parameters and have limited ability to model the increasingly dynamic nature of wildfires.In response to the escalating threat, our work addresses this shortcoming in short-term fire hazard prediction.First, we present a comprehensive and high fidelity remotely sensed active fire dataset fused from over 20 satellites.Second, we develop region-specific ML-based wildfire hazard prediction models for South America, Australia, and Southern Europe.The different models cover pixel-wise, spatial and spatio-temporal architectures, and utilize weather, fuel and location data.We evaluate the models using time-based cross-validation and can show superior performance with a PR-AUC score up to 44 times higher compared to the baseline FWI model. Using explainable AI methods, we show that these data-driven models are also capable of learning meaningful physical patterns and inferring region-specific wildfire drivers.