Artificial Intelligence (AI) is reshaping public mental healthcare by enabling personalized, preventive, and data-driven approaches to mental wellbeing. Traditional mental health systems are often reactive, focusing on treatment after symptoms become clinically significant. In contrast, AI technologies—including machine learning, natural language processing, predictive analytics, and digital phenotyping—offer opportunities for early detection, continuous monitoring, and individualized prevention strategies.

AI-driven systems can analyse behavioural, emotional, and physiological data collected through smartphones, wearable devices, electronic health records, and digital platforms to identify early indicators of depression, anxiety, stress, and suicidal behaviour. These technologies may improve mental healthcare accessibility, particularly among adolescents, underserved populations, and regions with limited psychiatric resources.

Despite these benefits, the rapid integration of AI into public mental health raises major ethical and societal concerns. Issues related to privacy, data security, algorithmic bias, digital inequality, and reduced human interaction remain significant challenges. Ethical governance and transparent regulatory frameworks are essential to ensure safe and equitable implementation.

This article examines the role of AI in advancing personalized prevention strategies in public mental health, highlighting current applications, benefits, ethical limitations, and future directions. The paper argues that AI should function as a supportive tool that enhances—not replaces—human-centred mental healthcare.

Keywords: Artificial Intelligence; Public Mental Health; Personalized Prevention; Digital Mental Health; Machine Learning; Digital Phenotyping; AI Ethics; Predictive Analytics