Soft tissues exhibit complex biomechanical behaviour arising from strong coupling between mechanical deformation, fluid transport, and underlying biological processes across multiple spatial and temporal scales(1,2). Capturing these interactions remains a central challenge in computational biomechanics, as conventional constitutive and continuum models often demonstrate limited reconciliation of the heterogeneous microarchitecture, functional grading, and time-dependent multi-physics effects(3–6). Recent advances in high-resolution imaging(7–11), data-driven methods(12–15), and physics-based simulations offer new opportunities to bridge these gaps(16,17). This contribution presents an integrated multiscale and multiphysics framework that combines image-derived tissue architecture, experimental characterisation, and reproducible computational modelling to improve predictive simulations of soft tissue behaviour. Central to this approach is Fit2Tissue(18), an open, community-driven platform for systematic calibration and benchmarking of constitutive models using labelled mechanical testing datasets and standardised experimental metadata. The platform enables automated fitting and comparison of widely used hyperelastic formulations and provides a structured repository for storing fitted parameters, experimental conditions, and tissue-specific information. Ongoing developments extend the framework toward anisotropic and time-dependent formulations, including poroelasticity and viscoelasticity, to better represent hydrated and fibre-reinforced tissues. The efficacy of this framework is demonstrated through the validation with open access data as well as the introduction of multivariate porcine meniscus datasets from biaxial testing and imaging. By embedding reproducible parameter identification within image-informed multiscale workflows, the proposed approach facilitates consistent linking of experimental data, constitutive modelling, and multiphysics simulations. This integration supports more realistic descriptions of deformation–transport interactions and enhances the reliability and transparency of computational predictions. The framework aligns with the objectives of multiscale and multiphysics soft tissue modelling and contributes to applications in tissue engineering, regenerative medicine, biomedical device design, and patient-specific clinical modelling.