Mental health disorders are a major global challenge, with traditional diagnostic and treatment methods often falling short due to symptom complexity and variability. Artificial Intelligence (AI) is emerging as a valuable tool, providing personalized, efficient, and accessible care. This review explores the use of AI algorithms like deep learning, support vector machines, and random forests in diagnosing and predicting mental health disorders. Ensemble and hybrid models have achieved impressive accuracy, often surpassing 90%. However, challenges include limited access to diverse, high-quality datasets, data privacy concerns, and the need for interpretable models. While AI shows promise in supporting mental infrastructure and accessibility issues, hinder health care in underserved areas, its implementation. This paper discusses the strengths and challenges of current AI applications in mental health, offering insights into future research directions. Emphasis is placed on developing more robust algorithms, creating inclusive datasets to reduce biases, and ensuring models are interpretable and integrated into clinical practice. Addressing these challenges will help AI contribute significantly to mental health care, improving diagnosis, treatment, and easing pressure on healthcare systems worldwide.