Identifying the psychological instability in mental health assessment through the application of ML techniques, using the principally the RFA (Random Forest Algorithm). This research investigates the application of machine learning techniques to detect psychological instability in individuals. By employing a variety of algorithms, including both supervised and unsupervised learning methods, this study aims to predict psychological states based on diverse data inputs such as behavioural patterns, physiological signals, and social interactions. The models are developed and validated using datasets from clinical studies, social media activity, and wearable health devices. The results illustrate the capability of ML to provide accurate and timely predictions of psychological instability, offering valuable insights for early diagnosis and intervention in mental health care. This study advances the field by demonstrating a data-driven approach to understanding and managing psychological health.