Real-time simulation of large-scale problems with disparate spatial and temporal scales, such as laser additive manufacturing and fatigue stress concentration analysis, remains computationally prohibitive with conventional methods. We propose the MultiLevel Variational MultiScale (ML-VMS) method, a novel approach that seamlessly integrates a multilevel mesh strategy into the Variational Multiscale (VMS) framework. The framework employs a global coarse mesh, while superimposing fine meshes in local regions, thereby creating a coupled multi‑level computational scheme. Solutions across scales are bidirectionally coupled through a variational formulation. A distinctive feature of our approach is the use of Convolution Hierarchical Deep‑learning Neural Networks (C‑HiDeNN) [1] as approximation functions, which achieve high‑order continuity and convergence rates without increasing the number of degrees of freedom. To further accelerate simulations, we integrate a space-time reduced‑order model (ROM) based on C‑HiDeNN‑Tensor Decomposition (TD) into the ML‑VMS framework, compressing degrees of freedom from O(n^d) to O(nd) (where n is the number of nodes in one dimension and d is the dimension). In a large‑scale transient heat‑transfer simulation of a single‑track LPBF process, equivalent to a full‑order finite element model with ~10^10 degrees of freedom, the three‑level ML‑VMS C‑HiDeNN‑TD ROM achieves a promising speedup of approximately 5,000x on a single CPU, compared to a single‑level linear FEM‑TD ROM. A 3D elasticity problem confirms the generality of the method, demonstrating both theoretical convergence rates and significant computational efficiency. The ML‑VMS framework, enhanced by tensor‑decomposition‑based model reduction, thus offers an efficient and scalable numerical tool for solving high‑dimensional, multi‑scale problems.