Age, sex, and body mass index (BMI) were associated with obstructive sleep apnea (OSA). Although various methods have been used in OSA prediction, this study aimed to develop predictions using simple and general predictors incorporating machine learning algorithms. This single-center, retrospective observational study assessed the diagnostic relevance of age, sex, and BMI for OSA in a cohort of 9, 422 patients who had undergone polysomnography (PSG) between 2015 and 2020. The participants were randomly divided into training, testing, and independent validation groups. Multivariable logistic regression (LR) and artificial neural network (ANN) algorithms used age, sex, and BMI as predictors to develop risk-predicting models for moderate-and-severe OSA. The training-testing dataset was used to assess the model generalizability through five-fold cross-validation. We calculated the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the independent validation set to assess the performance of the model. The results showed that age, sex, and BMI were significantly associated with OSA. The validation AUCs of the generated LR and ANN models were 0.806 and 0.807, respectively. The independent validation set's accuracy, sensitivity, specificity, PPV, and NPV were 76.3%, 87.5%, 57.0%, 77.7%, and 72.7% for the LR model, and 76.4%, 87.7%, 56.9%, 77.7%, and 73.0% respectively, for the ANN model. The LR- and ANN-boosted models with the three simple parameters effectively predicted OSA in patients referred for PSG examination and improved insight into risk stratification for OSA diagnosis.