Turbulent flow around bluff bodies like square cylinders involves complex vortex shedding and flow separation, challenging traditional computational methods. This study developed a novel approach using a generative artificial intelligence (GenAI) model to predict turbulent flow over a single square cylinder. The GenAI model was trained using high-fidelity simulation data generated from an advanced differentiable physics framework (PhiFlow), which can efficiently capture the nonlinear dynamics of turbulent flow. Flow predictions from the GenAI model were validated against numerical results, demonstrating high accuracy in capturing key flow characteristics, including vortex shedding frequency. Stability and spatial—temporal frequency analyses revealed strong agreement between the diffusion model and numerical simulations. This study highlights the potential of GenAI models to significantly enhance the prediction and analysis of turbulent flow, offering a powerful tool for fluid dynamics research and engineering applications.