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The KC-135R Stratotanker is a multifunction slim body aircraft that provides air refueling and airlift for the United States’ war and peacetime requirements, which demand a certain level of availability. As the KC-135R fleet ages, the aircraft availability (AA) rate degrades due to high demand use, stress, and the age of the equipment. Preventative and corrective maintenance is designed to return the aircraft to an available state to meet mission requirements, but the United States Air Force continues to fail at meeting every requirement communicated by commanders for KC-135R air refueling and airlift. Focusing on aircraft metrics can enable a prediction model of AA and provide the unit commanders the tools for data influenced decisions. The analysis of historical aircraft maintenance and flight metrics will show a correlation between tracked metrics and AA, thus, the ability to predict a future availability rate. Furthermore, analyzing the data with machine learning techniques will improve prediction accuracy by evaluating the variable importance and will make inferences learned from mining the data that are otherwise difficult to model in a complex system of systems.