This minisymposium provides a platform to discuss recent advances in machine learning and data-driven methodologies for material modeling, design, and manufacturing. With the rapid growth of artificial intelligence and high-performance computing, these approaches are transforming traditional paradigms in computational mechanics, materials science, and manufacturing engineering. The minisymposium brings together researchers developing and applying data-driven techniques to address complex multiscale and multiphysics problems. Key directions include the integration of physics-informed machine learning with continuum and discrete models, the development of surrogate and reduced-order models for efficient simulation, and data-driven discovery of constitutive laws and governing equations. Contributions leveraging experimental and industrial data for predictive modeling, digital twins, and real-time decision-making are particularly encouraged. Topics include, but are not limited to: • Machine learning-based surrogate models and foundation models for materials and manufacturing • Physics-informed and physics-guided machine learning for solid and structural mechanics • LLM and AI-agentic application for material modeling, design, and manufacturing • Data-driven computational mechanics without explicit constitutive laws • Discovery of constitutive relations and governing equations from data • Data-driven modeling of heterogeneous, anisotropic, and multiscale materials • Machine learning for inverse problems and parameter identification • Classical numerical method hybrid with AI/ML • Reduced-order modeling and real-time simulation • Integration of experimental data and AI for material characterization • Digital twins and smart manufacturing systems • Additive manufacturing and process optimization using data-driven approaches • Semiconductor manufacturing and micro/nano-scale material modeling • Uncertainty quantification, probabilistic modeling, and robustness analysis • Interpretable and explainable AI for engineering applications Applications may span solid/fluid mechanics, additive manufacturing, semiconductor processes, biomechanics, and other advanced material systems. Overall, this minisymposium aims to foster interdisciplinary dialogue and collaboration, highlighting the potential of machine learning and data-driven approaches to complement