This narrative review explores the comprehensive role of Artificial Intelligence (AI) in positive mental health, highlighting its potential to enhance mental healthcare through various applications. The paper begins by defining AI and its scope in mental health, followed by an examination of key AI components such as machine learning (ML), supervised and unsupervised learning, deep learning, natural language processing (NLP), and reinforcement learning. These technologies are then applied to various psychiatric disorders, including neurodegenerative disorders, intellectual disabilities, and seizures, as well as in awareness, diagnosis, and intervention in mental health conditions. The review discusses how AI can support individuals with mental health concerns by providing timely reminders, tracking side effects, and facilitating collaboration between patients and healthcare providers. It also highlights the use of AI in early detection and diagnosis of mental health disorders, personalized treatment planning, and the development of emotional intelligence through AI-driven platforms. However, the paper also addresses the limitations and ethical concerns associated with AI in mental health. These include data privacy and security, reliability and accuracy issues, potential biases, and the need for cultural sensitivity. The authors recommend enhancing the training and validation of AI algorithms with diverse datasets, implementing transparent and accountable AI systems, and integrating human oversight and collaboration in AI-based mental health interventions. In conclusion, while AI has shown significant promise in advancing mental health care, it is crucial to address these limitations and ethical concerns to ensure responsible and effective implementation.This narrative review explores the comprehensive role of Artificial Intelligence (AI) in positive mental health, highlighting its potential to enhance mental healthcare through various applications. The paper begins by defining AI and its scope in mental health, followed by an examination of key AI components such as machine learning (ML), supervised and unsupervised learning, deep learning, natural language processing (NLP), and reinforcement learning. These technologies are then applied to various psychiatric disorders, including neurodegenerative disorders, intellectual disabilities, and seizures, as well as in awareness, diagnosis, and intervention in mental health conditions. The review discusses how AI can support individuals with mental health concerns by providing timely reminders, tracking side effects, and facilitating collaboration between patients and healthcare providers. It also highlights the use of AI in early detection and diagnosis of mental health disorders, personalized treatment planning, and the development of emotional intelligence through AI-driven platforms. However, the paper also addresses the limitations and ethical concerns associated with AI in mental health. These include data privacy and security, reliability and accuracy issues, potential biases, and the need for cultural sensitivity. The authors recommend enhancing the training and validation of AI algorithms with diverse datasets, implementing transparent and accountable AI systems, and integrating human oversight and collaboration in AI-based mental health interventions. In conclusion, while AI has shown significant promise in advancing mental health care, it is crucial to address these limitations and ethical concerns to ensure responsible and effective implementation.