The model achieved 96.2% accuracy in pediatric classification, enhancing diagnostic precision through age-adaptive thresholds. By automating age estimation from CXRs, it bridges gaps in AI workflows, improving pediatric TB detection and enabling task-shifting to community health workers for more equitable, scalable screening in resource-limited settings.