Computational Fluid Dynamics has been a cornerstone of scientific computing for decades, enabling the simulation and analysis of complex flows in engineering, geosciences, and biomedical applications. While high-fidelity CFD methods continue to grow in accuracy and capability, their computational cost often limits exploration of large design spaces, real-time decision making, and uncertainty quantification. In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have emerged as novel, transformative tools for CFD, offering avenues to reduce computational costs [2], improve predictive accuracy [1], extract physical insight from data, and to build reduced-order or hybrid models [3]. However, despite rapid progress, the integration of ML/AI in CFD still faces fundamental open questions: ensuring physical consistency, generalization to out-of-distribution conditions, and effective use of limited or noisy data. This workshop aims to bring together leading experts from academia, industry, and research labs to: (i) Showcase state-of-the-art ML/AI approaches in CFD, from predictive surrogates to hybrid solver architectures. (ii) Discuss emerging applications in turbulence modeling, multi-phase flows, aerodynamics, and environmental flows. (iii) Address challenges and open questions such as model validation, uncertainty quantification, and reproducibility. In addition, our aim is to foster discussion, critical evaluation and collaboration across disciplines to advance the adoption of ML and AI methods in CFD. REFERENCES [1] D.A. Bezgin, A.B. Buhendwa, N. Adams: JAX-Fluids: A Fully-Differentiable High-order Computational Fluid Dynamics Solver for Compressible Two-phase Flows; Computer Physics Communications, 2023. [2] N.A.K. Doan, W. Polifke, L. Magri: Physics-informed Echo State Networks; Journal of Computational Science 47, 2020. [3] B. List, L.W. Chen, N. Thuerey: Learned Turbulence Modelling with Differentiable Fluid Solvers; Journal of Fluid Mechanics, 2022.