ABSTRACT Aims/Introduction Two Hong Kong Chinese non‐laboratory‐based prediabetes/diabetes mellitus (pre‐DM/DM) risk models were developed using logistic regression (LR) and machine learning, respectively. We aimed to evaluate the models' validity in case finding of pre‐DM/DM in a Chinese primary care (PC) population. We also evaluated the validity of a risk‐scoring algorithm derived from the LR model. Materials and Methods This was a cross‐sectional external validation study on Chinese adults, without a prior DM diagnosis, who were recruited from public/private PC clinics in Hong Kong. A total of 1,237 participants completed a questionnaire on the models' predictors. Of that, 919 underwent blood glucose testing. The primary outcome was the models' and the algorithm's sensitivity in finding pre‐DM/DM cases. The secondary outcomes were the models' and the algorithm's specificity, positive/negative predictive values, discrimination and calibration. Results The models' sensitivity were 0.70 (machine learning) and 0.72 (LR). Both showed good external discrimination (area under the receiver operating characteristic curve: machine learning 0.744, LR 0.739). The risks estimated by the models were lower than the observed incidence, indicating poor calibration. Both models were more effective among participants with lower pretest probabilities; that is, age 18–44 years. The algorithm's sensitivity was 0.77 at the cut‐off score of ≥16 out of 41. Conclusion This study showed the validity of the models and the algorithm for finding pre‐DM/DM cases in a Chinese PC population in Hong Kong. They can facilitate more cost‐effective identification of high‐risk individuals for blood testing to diagnose pre‐DM/DM in PC. Further studies should recalibrate the models for more precise risk estimation in PC populations. A cross‐sectional external validation study on 919 Chinese adults without a prior diabetes diagnosis recruited from public/private primary care clinics in Hong Kong. Our findings supported the external validity of the new non‐laboratory‐based models and the derived risk‐scoring algorithm in finding pre‐DM/DM cases in a primary care population.