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The area under the plasma concentration\u2010time curve over 24\u2009h (AUC<jats:sub>0\u201024<\/jats:sub>) of azithromycin correlates best with its effectiveness. The aim of this study was to evaluate the ability of machine learning (ML) to predict the AUC<jats:sub>0\u201024<\/jats:sub> of azithromycin in children with community\u2010acquired pneumonia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Various ML algorithms were used to build ML models based on simulated pharmacokinetic profiles from a published population pharmacokinetic model. A priori\u2010ML model predicted AUC<jats:sub>0\u201024<\/jats:sub> using patients' characteristics and after the trough concentration (<jats:italic>C<\/jats:italic><jats:sub>0<\/jats:sub>) became available, a posteriori\u2010ML model was built for improved prediction. Statistical methods and pharmacodynamic (PD) evaluation methods were used to evaluate the ML model's predictive accuracy in a real\u2010world study. ML\u2010optimized doses were evaluated by calculating the probability of PD target attainment in virtual trials compared with guideline\u2010recommended doses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The AUC<jats:sub>0\u201024<\/jats:sub> can be predicted by priori\u2010ML model using the CatBoost algorithm with dosing regimen and two covariates as predictors (weight, alanine aminotransferase) before initial administration. A posteriori\u2010ML model using CatBoost algorithm was built with adding <jats:italic>C<\/jats:italic><jats:sub>0<\/jats:sub> as a predictor. In real\u2010world validation, the mean absolute prediction error of the priori\u2010ML and posteriori\u2010ML models was less than 30%. 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