The arXiv preprint (v1, May 2026) introduces MHLF, a multigrid-hierarchical learning approach that initializes high-fidelity RANS simulations for complete three-dimensional aircraft geometries. Tested across three engineering-scale configurations spanning Mach 0.15–6.0, the method achieved 3–8× faster convergence while preserving numerical accuracy comparable to conventional cold starts. Unlike prior ML-CFD efforts limited to 2D airfoils, surface pressures, or simplified 3D shapes, MHLF maintains topological consistency across grid levels and explicitly models regional flow heterogeneity, addressing a key scalability barrier. This directly supports the editorial view that full-field ML prediction now offers concrete leverage on aerospace design cycles. Earlier attempts, such as those in Thuerey et al. (2020) on surrogate turbulence modeling and Wang et al. (2023) on graph neural network accelerators, stopped short of production aircraft grids; MHLF bridges that gap. Limitations include the preprint status (no peer review yet), restriction to steady RANS, and evaluation on only three geometries, leaving generalization to unsteady or multidisciplinary cases unproven. If validated, the approach could compress iterative optimization loops from weeks to days.