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Data were used from the National Poison Data System from 2014 to 2018, for patients 0\u201389\u2009years old with single\u2010agent exposure to eight drugs or drug classes (acetaminophen, aspirin, benzodiazepines, bupropion, calcium channel blockers, diphenhydramine, lithium and sulfonylureas). Four classifier prediction models were applied to the data: logistic regression, LightGBM, XGBoost, and CatBoost. There were 201\u2009031 cases used to develop and test the algorithms. Among the four models, accuracy ranged 77%\u201380%, with precision and F1 scores of 76%\u201380% and recall of 77%\u201378%. Overall specificity was 92% for all models. Accuracy was highest for identifying sulfonylureas, acetaminophen, benzodiazepines and diphenhydramine poisoning. F1 scores were highest for correctly classifying sulfonylureas, acetaminophen and benzodiazepine poisonings. Recall was highest for sulfonylureas, acetaminophen, and benzodiazepines, and lowest for bupropion. Specificity was &gt;99% for models of sulfonylureas, calcium channel blockers, lithium and aspirin. For single\u2010agent poisoning cases among the eight possible exposures, machine learning models based on clinical signs and symptoms moderately predicted the causal agent. 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