Wildfire ignition probability data provides spatially explicit estimates of the likelihood that a wildfire will start in a given location. The resulting datasets, specific to the Western and Southeastern U.S. regions, offer geospatial estimates of wildfire ignition probabilities, distinguishing between human-caused and natural (lightning) ignitions, as well as providing combined probabilities for both. The authors employ Random Forest machine learning, customized for probabilistic predictions, to model ignition likelihood based on spatial trends in observed fire occurrences, topographic features, climatic factors, vegetation characteristics, and human development patterns. The resulting datasets are scaled to recent observed ignition rates (e.g. 2006-2020 fire occurrence database) and have a spatial resolution of 120 meters. These datasets are a valuable resource for wildfire risk assessments (QWRA), risk mitigation planning, and decision support in land management, policy development, and other fire-related contexts.