Background and Aims: The fastest growing group of students with disabilities are those with Autism Spectrum Disorder (ASD). States annually report on post-high school outcomes (PSO) of exited students. This study sought to fill two gaps in the literature related to PSO for exited high-school students with ASD and the use of state data and predictive modeling. Methods: Data from two states were analyzed using two predictive analytics (PA) methods: multilevel logistic regression and machine learning. The receiver operating characteristic curve (ROC) analysis was used to assess predictive performance. Results: Data analyses produced two results. One, the strongest predictor of PSO for exited students with ASD was graduating from high school. Two, machine learning performed better than multilevel logistic regression in predicting PSO engagement across the two states. Conclusion: This study contributed two new and important findings to the literature: (a) PA models should be applied to state PSO data because they produce useful information, and (b) PA models are accurate and reliable over time. Implications: These findings can be used to support state and local educators to make decisions about policies, programs, and practices for exited high school students with ASD, to help them successfully transition to adult life.