Fires including wildfires harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size >=1 inch) in central US (CUS) over the 20-year period (2001- 2020) using machine learning (ML) methods, Random Forest (RF) and Extreme Gradient Boosting (XGB). The developed RF and XGB models demonstrate high accuracy (>90%) and F1-scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (i.e., WY, SD, NE, and KS). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). The results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on the case simulations