Applying AI-driven surveillance frameworks to detect emerging mental health risks among adolescents using integrated One Health environmental indicators

Escalating environmental stressors including climate variability, air pollution, water contamination, and ecological disruption are increasingly recognized as significant contributors to mental health challenges among adolescents. Yet, conventional public health monitoring systems remain fragmented, reactive, and insufficient for timely identification of emerging psychosocial risks. Artificial intelligence (AI) presents a transformative opportunity to bridge this gap by integrating environmental, human, and ecological indicators within the One Health framework to create advanced surveillance systems capable of detecting early mental health vulnerabilities. This article provides a comprehensive analysis of AI-driven surveillance architectures that leverage multimodal datasets such as satellite climate observations, urban pollution sensors, microbial and vector-distribution data, school health reports, and behavioural digital footprints to model adolescent mental health risk trajectories with high precision. The broader discussion highlights how machine learning, pattern-recognition algorithms, and anomaly-detection techniques can identify subtle environmental–behavioural interactions that precede clinical symptoms, including stress, anxiety, sleep dysregulation, and mood disturbances. As the analysis narrows, the paper demonstrates how integrated One Health surveillance models can stratify risk across communities, detect geographically clustered environmental triggers, and flag early shifts in mental well-being linked to temperature extremes, toxic exposures, allergens, zoonotic influences, and degraded living conditions. Additionally, the study explores how AI-enabled dashboards and early-warning systems could support schools, mental health practitioners, and policymakers by offering real-time indicators and predictive alerts. Ethical considerations including responsible data governance, adolescent privacy, algorithmic fairness, and transparent model communication are emphasized to ensure safe and equitable adoption. The findings argue that AI-driven surveillance systems anchored in One Health principles offer a crucial pathway toward proactive, preventive, and environmentally informed adolescent mental health strategies. Strengthening these systems will be essential for safeguarding youth resilience in an era marked by accelerating ecological and climatic instability.