The application of Artificial Intelligence (AI) to metal forming has shown significant potential in addressing long-standing challenges related to simulation time, process optimization, and material modelling [1, 2]. This Minisymposium invites contributions that explore the integration of data-driven methods with conventional numerical techniques to improve prediction, control, and robustness in metal forming processes. A key area of interest is the use of machine learning and surrogate models to accelerate computationally expensive simulations, particularly those involving non-linear constitutive behaviour and complex boundary conditions. Emphasis is also placed on methods that incorporate uncertainty quantification, enabling more reliable predictions in the presence of variability in material properties, process parameters, or operating conditions. Relevant topics include: • Data-driven material parameter identification and model calibration • Surrogate and reduced-order modeLling techniques for forming simulations • Hybrid approaches combining physics-based models with AI algorithms • Sensitivity analysis and uncertainty propagation • Transfer learning and domain adaptation across forming scenarios • Robust and multi-objective optimization under uncertainty • AI-based methods for real-time process monitoring and control References: [1] A.M. Habraken, T.A. Aksen, J.L. Alves et al., Analysis of ESAFORM 2021 cup drawing benchmark of an Al alloy, critical factors for accuracy and efficiency of FE simulations. International Journal of Material Forming 15 (2022) 61. [2] A.E. Marques, T.G. Parreira, A.F.G. Pereira, B.M. Ribeiro, and P.A. Prates, Machine learning applications in sheet metal constitutive Modelling: A review, International Journal of Solids and Structures 303 (2024) 113024.