Multi-omics models are increasingly used to integrate genomic, transcriptomic, epigenomic, proteomic, metabolomic, and cellular information for biomarker discovery, disease stratification, target prioritization, and therapeutic-response modeling. Yet strong performance within a development dataset does not establish that a model will retain its meaning or reliability when transferred to a different population, assay platform, laboratory workflow, tissue, disease state, or intervention context. Existing evaluation practices commonly compress transportability into one external performance measure, obscuring whether failure arises from inadequate population representation, incompatible measurement processes, unstable learned representations, altered biological relationships, or combinations of these factors. This article proposes a Multi-Omics Transportability Map as a conceptual and methodological architecture for characterizing source–target differences without collapsing them into a single domain-shift label. The map distinguishes four interacting shift axes—population, platform, laboratory, and biological context—from three diagnostic mismatch classes: representation, measurement, and biological mismatch. It further links these diagnoses to external validation, adaptation, recalibration, uncertainty assessment, restricted use, additional evidence collection, or abstention. A versioned evidence ledger and graded claim structure are proposed to preserve provenance, expose conflicting evidence, and align statements of generalizability with the target settings actually evaluated. The contribution is intended to support research design, model appraisal, and transparent pharmaceutical interpretation rather than to function as a validated prediction system or deployment rule. Its usefulness remains conditional on complete metadata, appropriate target data, domain expertise, experimentally credible biological evidence, and future prospective evaluation. By reframing transportability as a relational and multidimensional property, the proposed map may help prevent benchmark performance from being mistaken for cross-context pharmaceutical usefulness.