To address the core challenges of current adolescent scoliosis screening in school health monitoring systems—low manual efficiency, high missed diagnosis rates, and incomplete closed-loop monitoring—this study aims to empower school health monitoring systems with intelligent screening solutions. We developed an AI-powered scoliosis screening system integrating image recognition and 3D posture scanning technologies, piloting it in three middle schools to explore practical implementation pathways. Through analyzing the system’s technical framework (“data collection → intelligent analysis → result feedback → intervention tracking”) and standardized workflow (“semester-wide screening → focused follow-up → record updates”), we validated system optimization using pilot data: average screening efficiency per grade level increased by 40%, missed diagnosis rates dropped below 3%, and intervention follow-up rates rose from 35% to 82%. The study also identified challenges, including high hardware costs, data compatibility issues, and limited parental awareness, proposing targeted strategies like “tiered equipment configuration,” “standardized interface protocols,” and “home-school collaborative education.” These findings provide practical references for improving health monitoring systems in primary and secondary schools, while offering theoretical support for integrating smart technologies with school public health services.