The rise of mental health problems in children has created a need for early detection and intervention strategies. The routine method of diagnosing mental illness in child ren often relies on testing, which can lead to delays in treatment. Machine learning (ML) has become a powerful tool for analyzing complex data with the ability to identify subtle patterns associated with mental health. This article explores the potential of machine learning models for early detect ion of mental health problems in children, focusing on accuracy of facts, timeliness of intervention, and ethical considerations r elated to data privacy and algorithmic bias.