This research examines the integration of neuro-symbolic AI and quantum computing to enhance understanding of emotional regulation in trauma survivors. The primary goal is to develop complex models that replicate mental processes involved in emotional regulation, thereby improving treatment approaches. An interdisciplinary framework combines neural network learning and symbolic reasoning with quantum computing’s processing power. Secondary data from peer-reviewed articles and reputable sources were synthesized to validate these models, utilizing systematic literature reviews and advanced computational techniques while ensuring ethical standards. Results indicate that this integration significantly enhances understanding of emotional regulation, offering innovative theoretical insights and practical applications. The developed models enable personalized treatment plans that improve the effectiveness of mental health interventions. The findings suggest substantial potential for advancing mental health research and treatments, ultimately aiming to enhance the quality of life for trauma survivors. Future research should focus on acquiring primary data, exploring innovative aspects, and conducting longitudinal studies to assess the long-term impacts of these interventions.