The current food system is a major driver of climate change, biodiversity loss, and public health issues. Driven by increasing sustainability and health concerns, demand for plant-based diets and products continues to grow. However, the limited functional properties of plant proteins in replicating meat-like textures remain a significant barrier to consumer acceptance. Plant-based meat analog (PBMA) development remains constrained by trial-and-error formulation and process tuning, which is slow, costly, and difficult to scale. This project aims to deliver an AI-driven digital twin that couples soft-matter physics with multi-fidelity simulation and physics-informed learning to optimize protein structuring in real time. We will integrate finite volume (FVM), finite element (FEA), lattice Boltzmann (LBM), and physics-informed neural networks (PINNs) into a unified framework that links formulation and process variables to textural outcomes. Phase I establishes the physical backbone: we will measure the physicochemical, thermal, and rheological properties of pea protein (a sustainable, Nordic-abundant feedstock) via FTIR, DSC, and rheometry; calibrate constitutive models for thermo-shear history; and generate paired experimental–simulation datasets to train PINNs that predict microstructure and macroscopic texture from composition, temperature profile, screw speed, and die geometry. Phase II delivers the operational twin: a PINN-augmented controller enabling fast “what-if” exploration, adaptive recalibration as conditions drift, and automated set-point optimization via a dual-mode strategy that balances high-fidelity accuracy with real-time responsiveness. Uncertainty quantification and sensitivity analysis will guide robust control and ingredient substitution. Expected outcomes include (i) improved predictive accuracy for texture metrics (e.g., anisotropy index, chewiness, tensile strength), (ii) tighter process control with reduced waste, energy, and material use, and (iii) a transferable methodology for physics-informed AI in soft-matter manufacturing. By accelerating scale-up and consistency of PBMAs, the project advances sustainable production while supporting broader nutritional accessibility and consumer acceptance.