This article proposes a unified topology optimization framework based on the Cut Finite Element Method (CutFEM) and the Moving Morphable Component (MMC) method, primarily designed to overcome the computational efficiency bottlenecks in traditional high-fidelity topology optimization. Conventional methods often suffer from the "curse of dimensionality" and the heavy burden of mesh reconstruction, limiting their application in complex engineering iterations. First and foremost, to ensure high computational efficiency in standard optimization problems, our method leverages the explicit geometric parameterization of MMC. This drastically reduces the number of design variables from millions to hundreds, while the non-matching mesh technique of CutFEM decouples the geometry from the discretization. This combination allows components to evolve, deform, and merge freely without the need for repetitive background mesh reconstruction, significantly accelerating convergence speeds and geometric fidelity. Secondly, we extend this efficient framework to complex multi-material designs. By employing Nitsche’s method to weakly enforce interface continuity, the proposed approach eliminates the common pseudo-grayscale artifacts found in traditional implicit methods. It achieves precise material interface definitions and seamless multi-phase interactions without compromising the computational efficiency established in single-material cases. Finally, to address practical engineering challenges such as structural complexity and manufacturing constraints, we introduce a Morphologically-Informed Sensitivity Filtering (MISF) scheme. This novel regularization technique constructs a local morphological awareness field to modulate sensitivity, promoting a "synergetic coalescence" mechanism. MISF effectively suppresses chaotic, slender features and numerical noise, ensuring that the optimized structures are not only mathematically optimal but also robust and feasible for engineering applications. Numerical examples demonstrate the framework’s superior efficiency, versatility in multi-material problems, and robustness in handling complex structural topologies.