Artificial intelligence is increasingly being explored as a tool to accelerate pharmaceutical formulation development due to its ability to model complex relationships among formulation composition, process parameters, material attributes, and critical quality outcomes. This mixed-methods review aimed to map the landscape of AI applications in formulation optimization from 2017 to 2026 while assessing methodological quality, reproducibility, and translational readiness. Combining systematic evidence mapping with structured methodological appraisal, the review followed PRISMA-ScR principles and examined formulation types, AI methods, dataset structures, validation strategies, and evidence of implementation. AI applications were identified across solid oral dosage forms, lipid and polymeric nanoparticles, long-acting injectables, amorphous solid dispersions, and drug-release prediction. However, the evidence base remains dominated by retrospective datasets, internal validation, limited transparency, and few prospective or industrially integrated studies. While AI demonstrates clear technical potential for formulation optimization, stronger validation, improved reporting, shared datasets, and prospective workflow evaluation are essential before it can be reliably adopted in routine industrial or regulatory settings.