Ground-level ozone remains a persistent challenge in East Asian urban centers, where concentrations continue rising despite significant reductions in precursor emissions. Designing effective mitigation is complicated by the non-linear relationship between precursor abundance and reactivity. Standard data-driven approaches often suffer from "survivor bias," systematically undervaluing highly reactive precursors that are rapidly depleted. We introduce a process-informed machine learning framework that uses net chemical consumption (ΔVOC) rather than ambient concentrations to resolve this attribution failure. Applied to measurements from a NOₓ-saturated roadside site in Seoul, South Korea, the approach reveals a robust quantitative relationship between intrinsic hydroxyl radical reactivity (<em>k</em>OH) and ozone formation sensitivity.</p> <p>Across a comprehensive suite of precursors—including oxygenated VOCs (e.g., formaldehyde, acetone) and aromatics—the framework identifies reactive aromatics (trimethylbenzenes, xylenes) and biogenics (isoprene, monoterpenes) as the dominant kinetic drivers. Whereas static metrics such as OFP rank precursors by stoichiometric capacity under idealized accumulated conditions, the process-informed framework shows that realized ozone production in fresh urban plumes is governed by kinetic turnover rather than abundance, with this kinetic selectivity further amplified during high-ozone episodes. These results indicate that mass-based VOC inventories and OFP-style rankings, when applied without kinetic context, can systematically misallocate control priorities in NOₓ-saturated urban regimes. Because the framework requires only sub-hourly co-located VOC and ozone observations and no prescribed mechanism, it offers a complementary empirical pathway in settings where explicit mechanism-based modeling is constrained by incomplete VOC speciation or unmeasured radical precursors.