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The advent of computational and data-driven materials engineering has revolutionized the discovery and design of advanced materials, leveraging machine learning to navigate vast chemical spaces and predict properties from multimodal datasets. However, a critical challenge persists in the form of domain shifts, where AI models trained on one material class exhibit diminished predictive accuracy when inferred across disparate materials, undermining transferability in cross-material inference scenarios. This conceptual manuscript addresses this gap by introducing a novel framework that dissects the epistemic and computational underpinnings of such shifts within materials informatics ecosystems. Drawing from representation learning, graph neural networks, and uncertainty quantification paradigms, the proposed Cross-Material Inference Cascade (CMIC) framework conceptualizes domain shifts as emergent from mismatched representational hierarchies and inference pipelines, rather than mere data scarcity. It outlines structural layers for mitigating these shifts through adaptive representation alignments and feedback-driven discovery logics, without relying on empirical transfer learning techniques. Implications extend to high-throughput computation, autonomous discovery systems, and inverse design, fostering more resilient AI infrastructures in materials science. By emphasizing computational workflow dynamics and epistemic risk structures, this work provides interpretive insights for steering future data-driven paradigms toward robust cross-material predictions, enhancing the interoperability of foundation models and simulation-experiment couplings in the field.