Chronic pain in neurodivergent populations, particularly among individuals with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), remains significantly underdiagnosed despite increasing clinical evidence of elevated prevalence and functional impairment. This study proposes a computational epidemiology framework that integrates clinical meta-evidence, population-level inference, and behavioral neurophysiological proxies to model chronic pain burden in neurodivergent individuals. Drawing upon systematic and longitudinal findings, the framework synthesizes multidimensional risk factors including neurodevelopmental variability, pain perception dysregulation, and treatment responsiveness.

Evidence indicates that chronic pain is disproportionately prevalent in ADHD and autism populations, with significant quality-of-life implications (Asztély et al., 2019; Chruciel et al., 2023). Neurobiological hypotheses such as neuroinflammatory pathways and altered sensory processing provide mechanistic grounding (Kerekes et al., 2021). Moreover, pharmacological modulation of pain perception in ADHD populations further supports neurochemical involvement in pain sensitivity regulation (Bozkurt & Balta, 2023).

The proposed framework employs a hybrid epidemiological-ML architecture combining structured clinical data, survey-derived prevalence estimates, and intervention response variables to estimate latent chronic pain burden in neurodivergent cohorts. Findings from synthesized literature highlight systematic underreporting, diagnostic overshadowing, and heterogeneity in pain manifestation across age groups and neurodevelopmental conditions (Whitney & Shapiro, 2019; Han et al., 2024).

This research contributes a scalable computational model for public health surveillance, enabling early detection, stratified risk classification, and intervention optimization for chronic pain in neurodivergent populations.