Background/Objectives: Despite high sensitivity in deep learning models for identifying candidate locations of unruptured cerebral aneurysms (UCAs), excessive false-positive (FP) findings and insufficient characterization of their anatomical distribution remain barriers to clinical integration. We evaluated a multi-stage deep learning framework for reducing anatomy-specific FPs in TOF-MRA-based UCA screening. Methods: This retrospective multicenter study analyzed TOF-MRA images from 404 scans across seven institutions. A baseline model (Model-A) was compared with an updated model (Model-B) incorporating a secondary cluster-filtering algorithm. Reference standards were established by expert reviewers. Aneurysm-wise sensitivity was compared using McNemar’s test, FPs per case using the Wilcoxon signed-rank test, and FP distributions by vascular location. Results: Aneurysm-wise sensitivity was 94.3% (231/245) for Model-A and 93.9% (230/245) for Model-B, with 1.16 and 0.879 FPs per case, respectively, representing a significant 24% FP reduction (p < 0.001). Per-aneurysm sensitivity did not differ significantly (McNemar’s test, p = 1.00). Model-B suppressed mimics in the internal carotid artery (ICA) and middle cerebral artery (MCA). Aneurysms were most frequent in the ICA (29%) and MCA (22%). Setting Model-A FPs as 100%, ICA and MCA accounted for 47.1% and 11.3% in Model-A and decreased to 34.5% and 7.7% in Model-B. In an exploratory regional analysis with Holm adjustment, FP reductions remained significant in the ICA (adjusted p < 0.001) and MCA (adjusted p = 0.003). Conclusions: A secondary cluster-filtering algorithm reduced FP candidate findings by 24% with similar sensitivity point estimates (paired difference −0.4 percentage points, 95% CI −3.1 to +2.1). Region-specific error analysis revealed the anatomical dependency of FP reduction. The impact on reading workflow and patient outcomes requires prospective evaluation.