This project explores how Natural Language Processing (NLP) and Machine Learning (ML) can be harnessed to advance psychological assessment and intervention. Building on recent developments in computational psychology, the proposed research aims to develop scalable tools for screening mental health conditions, monitoring therapy processes, predicting clinical risk events, and modeling resilience and vulnerability. By extracting psychologically meaningful features from language data—ranging from social media posts to therapy transcripts—the project seeks to infer both state- and trait-level psychological variables and use them to forecast consequential outcomes such as relapse, transgressive behavior, or treatment response. Ultimately, the goal is to support proactive, personalized, and interpretable mental health care through linguistically grounded, data-driven modeling.