Published Paper Details:

Mental health has become a growing global concern, with increasing cases of stress, depression, anxiety, and emotional instability among various age groups, especially students and working individuals. Traditional psychological assessment methods rely heavily on face-to-face counselling, which may not always be accessible, affordable, or stigma-free. To address this, Artificial Intelligence (AI) provides a promising approach to predict mental health conditions using behavioural, social, and physiological data patterns. The proposed review focuses on AI-based mental health prediction systems that utilize machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Neural Networks to analyze user responses, lifestyle patterns, and sentiment from text or speech. This system aims to identify early warning signs and categorize individuals into mental risk levels such as mild, moderate, or severe. By enabling early detection, such AI-based tools can support timely medical attention, personalized recommendations, and self-care strategies. The review highlights key research advancements, challenges, dataset availability, and method accuracy. It also discusses future opportunities for integrating AI with wearable devices, real-time monitoring, and telehealth counselling support.