The integration of Artificial Intelligence (AI) into mental healthcare presents transformative opportunities for early diagnosis, personalized treatment, and continuous support. This chapter explores the practical implementation and integration of AI tools in mental healthcare, with an emphasis on adversarial generative and digital phenotyping techniques. It explores how AI contributes to early detection using electronic health records (EHRs), speech analysis, and behavioural monitoring. It also emphasizes personalized treatment through AI-driven recommendations, and highlights the continuous support offered by wearable devices and mobile applications. Real-world integration challenges, including data pre-processing, ethical concerns, and regulatory hurdles, are addressed alongside practical solutions. Some of the case studies demonstrate AI applications in depression detection, anxiety management, eye gaze tracking for Autism Spectrum Disorder (ASD), cognitive load assessment for workplace stress and suicide risk assessment using twitter data. The chapter concludes by discussing future directions, such as enhanced collaboration, advanced generative models, and global accessibility, emphasizing the need for innovation and thoughtful implementation to revolutionize mental healthcare.