Abstract: Recovering parameters of constitutive equations for complex fluids under Large Amplitude Oscillatory Shear (LAOS) poses significant challenges due to the scarcity of high-quality experimental data. This study proposes a multi-fidelity rheology-informed meta-modeling framework to address this issue, focusing on the Thixotropic Elasto-Visco-Plastic (TEVP) constitutive model. The framework integrates high-fidelity experimental data (D_HF) and low-fidelity synthetic data (D_LF) through a Multi-Fidelity Rheology-Informed Neural Network (MF-RhiNN). The MF-RhiNN architecture consists of a base subnet with a 4×20-layer fully connected neural network, it is trained using a composite loss function that combines data loss, initial condition loss, and residuals of equations. A multi-fidelity subnet, comprising both linear and nonlinear layers, is introduced to optimize parameter recovery. Low-fidelity data, generated via numerical interpolation of initial TEVP parameters, are used for pre-training. Meanwhile, high-fidelity LAOS datasets from colloidal glass, polymer micelles, soft gels, and marine clay are employed for incremental fine-tuning. Results demonstrate that the model successfully predicts nonlinear rheological hysteresis, as evidenced by close alignment between predicted and experimental Lissajous curves. Moreover, it reveals that the nonlinear weighting factor (w) significantly impacts prediction accuracy: w = 0.2 yields optimal performance, while higher weights introduce deviations, particularly under extreme strain conditions. Residual analysis confirms systematic errors in high-strain regions, highlighting areas for future improvement. Validation across diverse materials shows robust performance for colloidal glass and polymer micelles, with minor discrepancies observed in marine clay at high strains. The proposed framework effectively addresses data scarcity in rheological modeling, leveraging multi-fidelity data fusion to enhance computational efficiency and prediction reliability. This approach provides a practical solution for parameterizing complex constitutive models under LAOS.