Topology optimization is widely used in engineering design to identify efficient load paths and achieve lightweight structures. However, nominal designs (i.e., without considering failure scenarios) often neglect structural robustness, leading to limited redundancy and vulnerability to local failure. To address these limitations, fail-safe concepts have been incorporated into topology optimization frameworks. Despite recent progress, existing research predominantly focuses on discrete systems or 2D continua, whereas practical engineering structures are typically three-dimensional and large-scale, necessitating high-resolution analysis. This study systematically investigates fail-safe topology optimization for large-scale 3D continuum structures. To alleviate the substantial computational burden associated with 3D analysis, a recently developed problem-independent machine learning (PIML) technique is employed for acceleration. Representative numerical examples demonstrate that fail-safe designs differ significantly from their nominal counterparts. Specifically, optimal topologies for large-scale 3D continua tend to exhibit plate-shell or box-type features. These geometric characteristics mitigate the risk of catastrophic collapse triggered by local damage, thereby significantly enhancing overall structural integrity and robustness.