The rapid advancements in machine learning (ML) and artificial intelligence (AI) have significantly expanded the possibilities in computational mechanics. One of the areas most profoundly influenced by these technologies is the development of constitutive models for complex materials. ML and AI have revolutionized traditional approaches in this domain, offering innovative solutions to several challenging problems, including: Homogenizing the behavior of multiscale materials with intricate microstructures; discovering closed-form material models from large, diverse model libraries; solving inverse problems to identify heterogeneous material parameters, either by replacing the forward solver with ML surrogates or directly learning the inverse map from imaging data. These breakthroughs have been applied across a wide spectrum of materials, ranging from biological tissues (heart, skin, arteries, brain, etc.) to metals, elastomers, and soils. Beyond single-material modeling, recent Bayesian ML tools have been employed to account for the variability in material properties. This is particularly important for materials with inherent microstructural uncertainty or, in biological contexts, inter-individual variability in soft tissues. A key area of focus in ML-driven constitutive modeling is the integration of physics-based constraints with data-driven methods. While initial approaches have employed loss functions to enforce these constraints, more recent innovations have incorporated architectural features that ensure physics-consistent predictions across all parameter regimes. Similarly, efforts are underway to merge ML models with microstructure-informed modeling techniques, enhancing both interpretability and predictive power. ML and AI have demonstrated remarkable versatility in capturing a wide range of material behaviors, from simple linear elasticity to more complex phenomena like hyperelasticity, viscoelasticity, plasticity, and large-deformation, non-equilibrium processes. Furthermore, to bridge the gap between theory and application, these data-driven constitutive models need to be integrated into numerical solvers, such as finite element methods, to facilitate practical use in large-scale simulations. This symposium invites contributions in the area of machine learning-based constitutive modeling for all types of materials and behaviors (e.g., hyperelasticity, viscoelasticity, plasticity). We particularly encourage submissions that focus on probabili