Rocket landings on extraterrestrial landscapes face major challenges since the terrains are heterogeneous material surfaces, composed of regolith, rocks, and craters with complex multiscale behavior. Such variability undermines the stability of a rocket’s structure, making accurate landing essential. Artificial Intelligence presents favourable approaches that can enhance safety and effectiveness during these tasks. From a materials science perspective, these methods also represent data-driven multiscale modeling techniques, where terrain is treated as a responsive material system whose phases must be segmented and characterized. Prior studies demonstrate that AI can improve spacecraft navigation, surface characterization, and trajectory control. Semantic segmentation with Deeplab v3+, Bayesian deep learning for hazard detection, and reinforcement learning for trajectory optimization have all contributed to progress, but current approaches fail to fuse accurate terrain segmentation with predictive evaluation of optimal landing sites. This limitation parallels the gap in smart material research where micro- and macro-scale modeling often remain siloed. The proposed methodology addresses this by integrating U-Net with VGG-16 for extraterrestrial surface segmentation and an Extraterrestrial Landscape and Terrain Analysis (ELATA) model, a modified CNN for evaluating landing suitability. Collectively, these models function as a multiscale data fusion pipeline: segmentation provides micro-level material differentiation, while ELATA predicts macro-level structural interaction stability. Both models achieve high accuracy and demonstrate cross-surface generalization. This framework not only ensures that the optimal landing spot is identified for rockets but also illustrates a generalizable AI approach for smart materials modeling. By treating extraterrestrial terrain as a complex material system, the study highlights how AI-driven segmentation and predictive modeling can advance not only aerospace safety but also broader domains such as nanomaterials, biomaterials, and adaptive composites in Industry 4.0.