Natural ventilation remains a key strategy for improving indoor environmental quality (IEQ), lowering energy demand, and increasing resilience in low-rise residential buildings, especially in warm climates where mechanical ventilation is costly or unreliable. Classical ventilation studies are very often performed on computational fluid dynamics (CFD) or simplified thermal models, but they are computationally resource-heavy, data-dependent, or at odds with early design scenarios. Thus, this study proposes a Multi-Scale BIM-Driven Framework for Predictive Ventilation Opportunity Mapping (PVOM), presenting a geometry-based, data-light approach for investigating ventilation potential over micro-, meso-, and macro-scale spatial dimensions. Based on BIM models of two single-story residential buildings (Building A—author-developed and Building B—public reference model), the framework combines LOD 300 spatial modeling, multi-scale ventilation morphometrics, pathway prediction, and design optimization via opening repositioning, resizing, and envelope porosity adjustments. The outcomes indicate that PVOM correctly detects airflow constraints, stagnation pockets, and underperforming spaces, while simultaneously identifying geometrical areas for improvement on cross-ventilation. Performance for optimization scenarios indicated enhanced air change potential (ACH-P), cross-ventilation score (CVS), and spatial airflow continuity (SAC), thereby indicating the framework is adequate in facilitating early-stage sustainable design. This study presents a reproducible BIM-based method on natural ventilation assessment without CFD or advanced sensing systems, indicating PVOM as a scalable approach toward architects, engineers, and sustainability practitioners. BIM; natural ventilation; PVOM; ventilation morphometrics; low-rise buildings; sustainable design; performance optimization.