Virtual testing of composite laminates, typically used in the aeronautical industry, can be performed through progressive failure simulations at the meso-scale, i.e., at the level of the ply. These enable the accurate predictions of the failure mechanisms and strength values of laminate (notched) coupons that are employed for certification. To account for uncertainties (from material and geometry), a large number of detailed and costly simulations must be performed. At the same time, the ply properties and their uncertainty depend on their microstructure, i.e., on the fibres and matrix interactions. High-fidelity micro-mechanical numerical models of composite materials can provide important insights on the micro-mechanisms that lead to the onset and evolution of fracture in composite materials [1]. However, coupling the two-scales with high-fidelity finite element models would be prohibitive, especially in the context of uncertainty quantification. To overcome that, a set of Machine Learning-based surrogates at different scales is proposed towards a multi-scale Digital Twin. First, micro-mechanical models are used to determine homogenised ply properties and build databases of homogenised responses for data-driven surrogate models [2], which quickly predict the ply properties from micro-descriptors. Second, coupon (meso-scale) simulations are used to build databases of coupon strengths, which depend on laminate descriptors and ply properties. Similarly, these are employed to train surrogate models linking the meso (ply) to the macro (coupon)-scale. High-Performance-Computing strategies are used to generate the synthetic databases based on thousands of finite element simulations. Finally, the main objective is accomplished by linking the two proposed surrogates, linking the scales with models that are very quickly evaluated, and enable the propagation of uncertainty across the scales. This work has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101056682 (DIDEAROT project). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.