The G-WAVE Initiative seeks to democratize access to complex environmental data, enabling researchers to advance science in water security, energy production, public health, and transportation systems regardless of their technical background. Furthermore, G-WAVE supports education and broadening participation by creating a workforce development pipeline designed to engage a wide range of participants from across the Gulf region. By equipping a new generation of researchers with essential skills in artificial intelligence and data science, the project provides a replicable national model for building coastal resilience through technical training and community engagement. The technical goal of this planning project is to define the architecture and requirements for a scalable, AI-driven data ecosystem capable of integrating heterogeneous water data from diverse sources. The project methodology centers on three core technological pillars: predictive and process-guided AI, which integrates hydrodynamic and social models with physics-informed machine learning to accelerate hazard forecasting at a lower computational cost; AI-driven agents and operational intelligence, utilizing LLMs and agentic AI to provide natural language interfaces for complex data orchestration; and Gulf Digital Twin, a virtual representation of the region’s water systems used for robust "what-if" scenario planning. By addressing critical gaps in data interoperability and standardization, the G-WAVE Initia