<p>Early life stress is a significant risk factor for psychopathology; however, we lack scalable tools to identify youths who are most vulnerable. Here we tested whether the automated analysis of naturalistic speech can predict future mental health outcomes. We applied a multimodal suite of natural language processing techniques to comprehensive stress interviews with 204 youths (mean age 11.38 years, range 9–13 years; 58% female) to predict internalizing psychopathology up to 6 years later. We found that linguistic features robustly predicted future mental health, explaining more than twice the variance of traditional, human-rated risk factors. Across methods, linguistic style was more predictive than explicit emotional content. Importantly, we introduce a method to interpret transformer-based embeddings that revealed clinically intuitive themes of risk and resilience. Narratives of physical violence and social exclusion emerged as key markers of risk, whereas narratives of structured, routine activities and healthcare access were protective. Moreover, these data-driven semantic dimensions significantly predicted future diagnostic outcomes, outperforming expert ratings of cumulative stress severity. This study computationally analyzes detailed stress narratives to predict the onset of psychopathology across adolescence. Our findings establish a scalable framework to identify objective risk markers and novel intervention targets, demonstrating how artificial intelligence can enrich developmental clinical science.</p>