The powers that artificial intelligence (AI) has developed are impressive, with recent success in leveraging human expertise at various stages of model development. AI can attain its full potential only if, as part of its intelligence, it also actively teams with humans to co-create solutions. Combining AI simulation with human intelligence through data convergence can improve decision-making processes and provide a capacity akin to a "teaming intelligence." This research, for the first time, introduces the concepts of Human-AI Convergence (HAC) capabilities for flood evacuation decision-making. The objective of this study was to develop a unique, computationally effective surrogate HAC system for flood evacuation decision-making that integrates the distinctive features of AI with transportation geospatial data, a river hydraulic model, and human data from X (previously Twitter) to visualize flood inundation areas and suggest re-routing. The HAC system is smartly designed to forecast flood stage levels using AI across the US Geological Survey gauging stations and combine the results with Manning's equation results and transportation data, integrated into a web-based Google Earth visualization architecture. The technology has been tested in the Lowcountry of South Carolina, where previous flooding disasters caused considerable damage to the transportation networks and increased traffic on evacuation routes. This state-of-the-art HAC system— a flood evacuation product— stands to advance the frontier of human-AI collaborative research in the context of real-time flood emergency management and response.