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Depression, as one of the important public health issues worldwide, is the main cause of illness and disability in children and adolescents aged 10 – 19 years, leading to heavy economic and social burden. With the rapid development of artificial intelligence technology in recent years, the use of machine learning or deep learning methods to automatically identify depression and establish predictive models has provided a new perspective for depression screening. This study summarized previous domestic and foreign research, elucidating the research progress of screening methods and predictive models for depression in children and adolescents, and providing a scientific basis for improving the efficiency of depression screening, early identification, and intervention in children and adolescents.