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OBJECTIVE This study aimed to establish a prediction model of quetiapine concentration in patients with schizophrenia and depression, based on real-world data via machine learning techniques to assist clinical regimen decisions. METHODS A total of 650 cases of quetiapine TDM data from 483 patients at the First Hospital of Hebei Medical University from November 1, 2019, to August 31, 2022 were included in the study. Univariate analysis and sequential forward selection (SFS) were implemented to screen the important variables influencing quetiapine TDM. After ten-fold cross validation, the algorithm with the optimal model performance was selected for predicting quetiapine TDM among 9 models. SHapley Additive exPlanation was applied for model interpretation. RESULTS Four variables (daily dose of quetiapine, type of mental illness, sex and CYP2D6 competitive substrates) were selected through univariate analysis (p < 0.05) and SFS to establish the models. The CatBoost algorithm with the best predictive ability [mean (SD) R2 = 0.63±0.02, RMSE = 137.39±10.56, MAE = 103.24±7.23] was chosen for predicting quetiapine TDM among 9 models. The mean (SD) accuracy of the predicted TDM within ±30% of the actual TDM was 49.46±3.00%, and of the recommended therapeutic range (200-750 ng·ml-1 ) was 73.54±8.3%. Compared with PBPK model in previous study, the CatBoost model shows slightly higher accuracy within ±100% of the actual value. CONCLUSION This work is the first real-world study to predict the blood concentration of quetiapine in patients with schizophrenia and depression using artificial intelligent techniques, which is of significance and value for the clinical medication guidance.