Space traffic management is difficult. Monitoring and predicting the accurate, real-time position of satellites for collision avoidance in low earth orbit is an engineering challenge. The dominant source of error in satellite prediction tracking models is in the determination of space weather driven atmospheric drag. The High Accuracy Satellite Drag Model (HASDM) (Storz et al., 2005) was developed by the U.S. Air Force Space Command (AFSPC) between 2000–2005 to help solve this problem. It is a data assimilative modeling system using the JB2008 thermospheric density model (Bowman et al., 2008) plus continuously derived densities from several dozens of calibration satellites to achieve <5% density uncertainty at most epochs. The HASDM data is being made available to the community of scientists and operators for the first time. Under authority from the AFSPC, Space Environment Technologies (SET) has extracted two solar cycles of operational High Accuracy Satellite Drag Model (HASDM) data for scientific use and this is called the SET HASDM database. Navigating and extracting information from this database quickly and efficiently to manage satellite space traffic is currently complex and tedious. We present the development of a data-driven model for the HASDM mass density using Machine Learning and an attempt to quantify the associated uncertainties.