Introduction: No type 2 diabetes risk prediction models offer the option to predict individualized risk conditional on initiating different preventive interventions. Objective: Develop and externally validate an individualized diabetes risk prediction model with preventive intervention effects. Methods: The derivation cohort included participants in the Diabetes Prevention Program (DPP) trial randomized to placebo, metformin, or intensive lifestyle intervention (N=2640). A risk prediction model for incident diabetes was developed using Cox proportional hazards regression using clinically available predictors: sex, glycated hemoglobin, fasting plasma glucose (FPG), body mass index (BMI), triglycerides, and intervention. The model was individualized by including pairwise interactions between intervention and age, FPG, and BMI. The discrimination, calibration, and net benefit of the model’s 3-year predictions for incident diabetes were internally validated within the DPP using 10-fold cross validation, and externally validated among participants with prediabetes in the Multi-Ethnic Study of Atherosclerosis (MESA; N=2104). Results: In DPP and MESA, mean (standard deviation) age was 51 years (11) and 64 (10) and 67% and 50% of participants were women, respectively. The mean C-statistic was 0.71 (95% confidence interval [CI]: 0.68, 0.74) in DPP and 0.86 (95% CI: 0.83, 0.88) in MESA. The optimal intervention (lowest 3yr risk) was lifestyle for 86% and 97% of DPP and MESA participants, respectively, and metformin for the remaining. When lifestyle was optimal, 3-year risk was 10% in DPP and 7% in MESA. Conclusion: Individualized predictions that forecast risk of incident diabetes after initiating a preventive intervention may improve clinical decision-making and prevention.