Purpose:　This　study　introduces　GraFMRI,　a　novel　framework　designed　to　address　the　challenges　of　reconstructing　high-quality　MRI　images　from　undersampled　k-space　data.　Traditional　methods　often　suffer　from　noise　amplification　and　loss　of　structural　detail,　leading　to　suboptimal　image　quality.　GraFMRI　leverages　Graph　Neural　Networks　(GNNs)　to　transform　multi-modal　MRI　data　(T1,　T2,　PD)　into　a　graph-based　representation,　enabling　the　model　to　capture　intricate　spatial　relationships　and　inter-modality　dependencies.　Methods:　The　framework　integrates　Graph-Based　Non-Local　Means　(NLM)　Filtering　for　effective　noise　suppression　and　Adversarial　Training　to　reduce　artifacts.　A　dynamic　attention　mechanism　enables　the　model　to　focus　on　key　anatomical　regions,　even　when　fully-sampled　reference　images　are　unavailable.　GraFMRI　was　evaluated　on　the　IXI　and　fastMRI　datasets　using　Peak　Signal-to-Noise　Ratio　(PSNR)　and　Structural　Similarity　Index　Measure　(SSIM)　as　metrics　for　reconstruction　quality.　Results:　GraFMRI　consistently　outperforms　traditional　and　self-supervised　reconstruction　techniques.　Significant　improvements　in　multi-modal　fusion　were　observed,　with　better　preservation　of　information　across　modalities.　Noise　suppression　through　NLM　filtering　and　artifact　reduction　via　adversarial　training　led　to　higher　PSNR　and　SSIM　scores　across　both　datasets.　The　dynamic　attention　mechanism　further　enhanced　the　accuracy　of　the　reconstructions　by　focusing　on　critical　anatomical　regions.　Conclusion:　GraFMRI　provides　a　scalable,　robust　solution　for　multi-modal　MRI　reconstruction,　addressing　noise　and　artifact　challenges　while　enhancing　diagnostic　accuracy.　Its　ability　to　fuse　information　from　different　MRI　modalities　makes　it　adaptable　to　various　clinical　applications,　improving　the　quality　and　reliability　of　reconstructed　images.