This narrative literature review examines the growing field of using artificial intelligence (AI) to detect depression and anxiety, from intricate algorithms to practical applications. AI has emerged as a game-changer in early identification and intervention for these mental health disorders. The review highlights the current understanding of AI, its types, and its applications in various mental health disorders, including advantages, disadvantages, and future potential. By critically analyzing prominent scholarly works, the study aims to provide a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development. The review covers various applications of AI in mental health, such as chatbots and virtual assistants for initial patient screenings, wearable and mobile sensors for monitoring sleep patterns and activity levels, neuroimaging analysis for identifying biomarkers, personalized treatment recommendations, remote diagnosis and support, and digital therapeutic interventions. It also discusses the challenges and limitations of using AI, including data privacy, accuracy, and ethical concerns. The review concludes that AI can be used to diagnose mental health disorders, develop personalized treatment plans, and assist clinicians in decision-making. However, it emphasizes the need for addressing drawbacks and challenges, such as the need for human expertise, data privacy, and bias, to ensure effective and responsible implementation of AI in healthcare. The future of mental health diagnosis and treatment lies in a collaborative approach where AI and human expertise work together to improve patient care.This narrative literature review examines the growing field of using artificial intelligence (AI) to detect depression and anxiety, from intricate algorithms to practical applications. AI has emerged as a game-changer in early identification and intervention for these mental health disorders. The review highlights the current understanding of AI, its types, and its applications in various mental health disorders, including advantages, disadvantages, and future potential. By critically analyzing prominent scholarly works, the study aims to provide a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development. The review covers various applications of AI in mental health, such as chatbots and virtual assistants for initial patient screenings, wearable and mobile sensors for monitoring sleep patterns and activity levels, neuroimaging analysis for identifying biomarkers, personalized treatment recommendations, remote diagnosis and support, and digital therapeutic interventions. It also discusses the challenges and limitations of using AI, including data privacy, accuracy, and ethical concerns. The review concludes that AI can be used to diagnose mental health disorders, develop personalized treatment plans, and assist clinicians in decision-making. However, it emphasizes the need for addressing drawbacks and challenges, such as the need for human expertise, data privacy, and bias, to ensure effective and responsible implementation of AI in healthcare. The future of mental health diagnosis and treatment lies in a collaborative approach where AI and human expertise work together to improve patient care.