A new framework modifies the Laplacian operator in graph diffusion to enhance fairness by incorporating subspace projections, spectral adjustments, and frequency-based filtering. The method leverages graph diffusion's smoothing properties to mitigate bias, with theoretical analysis and empirical validation on synthetic and real-world datasets showing improved fairness without significant computational overhead.