Artificial intelligence offers transformative potential for nanomedicine design because nanoparticle performance emerges from complex interactions among composition, structure, processing, and biological context. Yet the translational impact of these models remains uncertain. This mixed-methods review maps the evidence on AI models for nanomedicine design and appraises their methodological quality and translational readiness. It focuses on nanoparticle formulation, drug delivery systems, nanocarrier optimization, biodistribution, and nano-toxicity prediction. The review combines systematic literature retrieval, descriptive evidence mapping, and critical methodological appraisal. Extracted domains included model type, nanoparticle class, prediction task, dataset source, validation design, reproducibility, interpretability, and proximity to in-vitro, in-vivo, or clinical translation. AI models have been applied across lipid, polymeric, inorganic, dendrimer-like, hybrid, and drug nanoparticle systems. However, methodological rigour is inconsistent, with frequent reliance on small datasets, internal validation, limited code or data sharing, and rare translational case studies. The field has demonstrated convincing proof-of-concept for AI-assisted nanomedicine design, but a substantial gap remains between computational prediction and clinically actionable formulation development. Progress will depend on prospective validation, standardized datasets, reproducible reporting, and stronger integration between computational and experimental teams.