Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts (i.e., Blacks or African Americans, non-Latino Whites, American Indians or Alaskan Natives, Asians, Native Hawaiians or other Pacific Islanders, and Latinos). Methods: Eleven years of Rehabilitation Services Administration (RSA)-911 case record data (fiscal year [FY] 2004 through 2014) on SVRA employment outcomes were extracted and entered into the ARIMA model and GM to test their predictive performance. Results: The GM was demonstrated to be superior to the ARIMA model in predictive accuracy performance. Remarkably, although the GM (1, 1) 3-year frequency curve projection simulation results (FY 2015-2017) showed a slight upward trajectory in the number of successfully rehabilitated Latinos compared to baseline FY 2014 actual numbers, more drastic downward trajectories were projected for Blacks or African Americans, non-Latino Whites, American Indians or Alaskan Natives, Asians, and Native Hawaiians or other Pacific Islanders. Conclusions: The GM represents a demonstrably capable and promising forecasting tool that could be useful to SVRA leaders, policy makers, advocates, and researchers in simulating predictions that inform future policy initiatives, influence strategic plan development, and help guide the state of the science on future research and development foci. Additional multiple comprehensive demonstration trials, nonetheless, are needed to either confirm or refute the GM's veracity in national and state predictive accuracy and curve fitting performance.