The phenomenon of adolescent suicide has become a serious challenge to global public health, and suicidal behavior can directly threatening the lives of adolescents.Attempted suicide during adolescence has long-term negative impacts on their health in adulthood.With the continuous development of artificial intelligence technology, machine learning has demonstrated markedly superior performance compared to traditional assessment tools in predicting the risk of suicide among adolescents.Therefore, this article reviews the application and significance of machine learning in predicting suicidal behavior among adolescents.It mainly focuses on machine learning-related concepts, the utilization of multimodal data such as text and voice, as well as the in-depth analysis of algorithmic performance.However, this learning technology continues to encounter challenges pertaining to data quality, overfitting, model interpretability as well as ethical considerations.In future practical applications, various factors, including data characteristics, problem requirements, time costs and algorithm performance, should be comprehensively considered to develop a more accurate predictive model for adolescent suicidal behavior, thereby safeguarding the mental health of adolescents.

The phenomenon of adolescent suicide has become a serious challenge to global public health, and suicidal behavior can directly threatening the lives of adolescents.Attempted suicide during adolescence has long-term negative impacts on their health in adulthood.With the continuous development of artificial intelligence technology, machine learning has demonstrated markedly superior performance compared to traditional assessment tools in predicting the risk of suicide among adolescents.Therefore, this article reviews the application and significance of machine learning in predicting suicidal behavior among adolescents.It mainly focuses on machine learning-related concepts, the utilization of multimodal data such as text and voice, as well as the in-depth analysis of algorithmic performance.However, this learning technology continues to encounter challenges pertaining to data quality, overfitting, model interpretability as well as ethical considerations.In future practical applications, various factors, including data characteristics, problem requirements, time costs and algorithm performance, should be comprehensively considered to develop a more accurate predictive model for adolescent suicidal behavior, thereby safeguarding the mental health of adolescents.