<p>Chronic pain is recognized as a disorder of distributed brain networks rather than the consequence of persistent nociceptive input. Among these networks, the medial prefrontal cortex (mPFC) is a key integrative hub linking sensory processing with affective, cognitive, and stress-related dimensions of pain. Evidence from neuroimaging, neurochemical, and longitudinal studies indicates that mPFC dysfunction contributes to impaired top-down modulation, altered emotional regulation, and the persistence of pain states. Nevertheless, these findings should be interpreted within a system-level framework, as mPFC activity reflects network reorganization rather than serving as an isolated or validated clinical biomarker. Moreover, the generalizability of mPFC-centered models is limited across patient populations, including children and those with psychiatric comorbidities or cognitive impairment. This editorial critically examines the neurobiological basis of mPFC-centered network dysfunction in chronic pain and discusses its implications for translational research, with a key focus on artificial intelligence (AI). These technologies are framed not as a near-term clinical solution but as enabling and exploratory methods for integrating multimodal data and modeling complex brain–behavior relationships. Emerging generative AI approaches, agent-based models, and digital twins can also be implemented as conceptual tools for hypothesis generation and in silico exploration of individualized network dynamics, rather than as established clinical applications. Although AI-based approaches may accelerate hypothesis generation and the identification of latent network-level patterns, their clinical relevance is currently constrained by key methodological challenges, including limited generalizability, imperfect phenotypic classification, the absence of robust ground truth for pain, and the need for extensive external and longitudinal validation.</p><p><b>Trial registration</b> Not applicable.</p>