African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRS_AA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRS_AA with other published scores. Results show that a 1-SD increase in the PRS_AA was associated with 40–60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37–1.88; OR 1.40, 95% CI 1.16–1.70; and OR 1.45, 95% CI 1.30–1.62) across three testing cohorts. These models captured 1.0–2.6% of the variance (R²) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRS_AA,ingeneral, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population.