Heavy-vehicle braking on long downhill roads cannot be assessed from component condition alone because adequate pressure and temperature may not ensure sufficient vehicle-level deceleration. This study develops a layered simulation framework for a representative 18,000 kg heavy vehicle that combines multisignal monitoring, deterministic Brake Control Unit (BCU) intervention, and offline HMI-derived machine-learning (ML) evaluation with separate decision authority. Matched OPEN_LOOP–BCU_ONLY cases evaluate intervention effects, whereas run-grouped tests assess ML performance. In a composite friction/slip case, 15.45 s of accumulated wheel lock occurred despite noncritical pressure and temperature. BCU intervention reduced duration above the 5 km/h near-stop threshold by 71.46% and 71.51% in Medium and Hard cases. Critical OPEN_LOOP did not reach 5 km/h within the 120 s horizon. Offline ML achieved F1-CRITICAL = 0.9984 for represented mechanisms but only 0.1432–0.7584 in post-selection mechanism holdouts. The novelty lies in jointly evaluating vehicle-level effectiveness, bounded deterministic intervention, and mechanism-coverage limitations within separated evidence streams. Results support multisignal monitoring and deterministic intervention, but represented-domain ML performance does not establish transfer to unseen mechanisms. Conclusions are limited to the evaluated simulation domain; ML remained offline and had no control authority.