Trauma exposure is common, unevenly distributed, and strongly associated with chronic disease, mental illness, and avoidable acute care, yet integrated care programs in underserved urban settings struggle to identify patients who need outreach before a crisis occurs. Artificial intelligence (AI) can help prioritize outreach, but predictive models trained on health-system data can reproduce, and even amplify, the access barriers that define underserved populations. This paper presents a multicenter implementation framework for AI-driven predictive modeling in trauma-informed integrated care and a technical validation of its predictive and equity-audit components. The framework aligns model design with trauma-informed principles and a hybrid stepped-wedge evaluation. The technical validation uses a synthetic cohort of 16,000 patients across six simulated urban sites, built to mimic access-dependent label bias; it demonstrates the method and is not an empirical finding about real patients. A model trained on recorded crisis contacts reached 30.4% of true-need patients with limited access to care at a 10% outreach capacity, compared with 47.1% of patients with adequate access (gap 16.7 percentage points; 95% CI 13.1 to 20.3), even though its AUC was 0.83. Training on clinically adjudicated need reduced the access gap to 2.3 points (95% CI −1.8 to 6.1), and need-proportional allocation redistributed outreach across deprivation strata. In leave-one-site-out validation, AUC ranged from 0.81 to 0.84 but calibration intercepts ranged from −0.22 to +0.49, so local recalibration is essential. Safe use of AI in trauma-informed care depends on label choice, equity auditing, recalibration, and governance more than on algorithm choice, and a real multicenter study must now measure the implementation outcomes specified here.