Predictive Memory Localization (PML) is a novel approach that treats the measured-grid intervention path as the predictive object of memory localization. PML separates random-calibrated target movement from semantic-neighbor and capability damage, and compares static localization and supervised geometry with a strength-disjoint low-dose causal response. The results show that PML can achieve 13.1% target-any and 12.3% clean-any, exceeding random by 3.6 and 3.4 percentage points. Across record-, dataset-, and domain-grouped splits, responses at $|\alpha|=0.1$ are the strongest signal for outcomes at disjoint strengths $|\alpha|\in\{0.25,0.5\}$. A predictor-driven selector can choose a coefficient or abstain, improving utility and reducing semantic-neighbor damage, and avoiding most evaluations in a dense scan. Across three residual-norm-matched base models, learned directions retain selective-path gains and low-dose responses yield 0.801-0.828 record-held-out macro AUROC. Thus, PML turns memory localization into a falsifiable forecast of margin-level selective outcomes and a risk-aware intervention decision. Blogger's Review: This paper proposes a novel approach called Predictive Memory Localization, which predicts selective intervention paths from internal signals. This approach can effectively predict target-any and clean-any outcomes and shows strong signals across record, dataset, and domain splits. The application prospects of this approach are broad, especially in scenarios that require prediction and intervention.