Green hydrogen is becoming a strategic industrial fuel for Saudi Arabia because it can convert world-class solar and wind resources into exportable molecules, decarbonized feedstocks, and resilient energy services for industrial cities. This review paper develops an AI-driven optimization perspective for green hydrogen production networks in NEOM, Yanbu, Jubail, Ras Al-Khair, Dammam and related logistics corridors under Vision 2030. The aim is to explain how machine learning, digital twins, stochastic optimization, and explainable decision systems can reduce cost, water stress, renewable curtailment, electrolyzer degradation, and offtake uncertainty while strengthening industrial competitiveness. A structured review methodology was used to synthesize recent literature, policy reports, and applied hydrogen system studies published between 2020 and 2025. The paper proposes an integrated framework that links renewable forecasting, desalinated water allocation, electrolyzer scheduling, storage sizing, ammonia conversion, port export planning, and ESG reporting. Findings show that AI is most valuable when it is embedded across the full network rather than limited to plant-level control. The study concludes that Saudi industrial cities can accelerate green hydrogen deployment by adopting interoperable data standards, bankable optimization models, cybersecure control rooms, and phased implementation roadmaps aligned with Vision 2030.