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This paper introduces MuFiNNs, a hierarchical multi-fidelity neural network framework for predicting 3D flame wrinkling and turbulent burning velocity using sparse experimental data. The approach integrates low-fidelity physical trends with high-fidelity corrections to enable robust prediction and extrapolation in data-limited combustion regimes.