Generative artificial intelligence in pharmaceutical science is commonly evaluated through its capacity to create novel molecular structures, yet therapeutic design depends on a wider evidence environment that includes protein sequence and conformation, cellular and multi-omics states, biomedical images, scientific text, target context, and experimental observations. The resulting literature is heterogeneous in terminology, model architecture, generated output, conditioning strategy, and validation depth, making conventional effect synthesis inappropriate and encouraging potentially misleading comparisons between representation learning, prediction, generation, and therapeutic development. This scoping review maps how multimodal generative approaches are defined, implemented, and evaluated across pharmaceutical data modalities. The review uses transparent eligibility, study-selection, data-charting, and terminology-mapping principles to distinguish modality integration from cross-modal generation and to separate demonstrated model capability from benchmark performance, experimental confirmation, translational value, and routine pharmaceutical readiness. The synthesis identifies a field with comparatively mature methods for molecular generation, emerging experimental evidence for protein design, developing applications in perturbational omics, and primarily enabling roles for biomedical images and scientific text. It further distinguishes conditioning, alignment, fusion, and cross-modal decoding as related but non-equivalent operations. An original review-derived taxonomy and evidence-maturity interpretation are proposed to organize these approaches; neither is presented as empirically validated. Major limitations include narrow reference datasets, inconsistent definitions of validation, limited external testing, weak causal grounding, sparse negative evidence, and insufficient connection between computational outputs and prospective therapeutic decisions. Multimodal generative systems may broaden therapeutic design beyond molecule creation, but their scientific value remains conditional on claim-matched evaluation, experimentally grounded validation, transparent provenance, and explicit boundaries between computational plausibility and pharmaceutical usefulness.