This research paper investigates the potential of AI-driven predictive analytics for the early detection of high-functioning anxiety symptoms. Since high-functioning anxiety is not officially recognized in the DSM-5, diagnosing and treating it can be extremely difficult. The research highlights that early identification is crucial to prevent the escalation of mental health issues such as depression and PTSD. This study explores many data sources, including physiological signals, behavioral patterns, and complex machine-learning algorithms used to identify subtle indicators of high-functioning anxiety. According to the research performed, AI can considerably improve diagnosis accuracy over conventional techniques, allowing for prompt, individualized therapies. To effectively integrate AI into mental health treatments, some obstacles must be addressed, like checking the diversity and quality of data, resolving ethical concerns, and protecting patient privacy. This study emphasizes the importance of collaboration among ethicists, mental health professionals, and AI researchers to create practical and ethical AI applications. Ultimately, the study aims to enhance understanding of high-functioning anxiety and the role of AI in mental health diagnostics.