Generative　adversarial　networks　(GAN),　which　are　fueled　by　deep　learning,　are　an　efficient　technique　for　image　reconstruction　using　under-sampled　MR　data.　In　most　cases,　the　performance　of　a　particular　model＇s　reconstruction　must　be　improved　by　using　a　substantial　proportion　of　the　training　data.　However,　gathering　tens　of　thousands　of　raw　patient　data　for　training　the　model　in　actual　clinical　applications　is　difficult　because　retaining　k-space　data　is　not　customary　in　the　clinical　process.　Therefore,　it　is　imperative　to　increase　the　generalizability　of　a　network　that　was　created　using　a　small　number　of　samples　as　quickly　as　possible.　This　research　explored　two　unique　applications　based　on　deep　learning-based　GAN　and　transfer　learning.　Seeing　as　MRI　reconstruction　procedures　go　for　brain　and　knee　imaging,　the　proposed　method　outperforms　current　techniques　in　terms　of　signal-to-noise　ratio　(PSNR)　and　structural　similarity　index　(SSIM).　As　compared　to　the　results　of　transfer　learning　for　the　brain　and　knee,　using　a　smaller　number　of　training　cases　produced　superior　results,　with　acceleration　factor　(AF)　2　(for　brain　PSNR　(39.33);　SSIM　(0.97),　for　knee　PSNR　(35.48);　SSIM　(0.90))　and　AF　4　(for　brain　PSNR　(38.13);　SSIM　(0.95),　for　knee　PSNR　(33.95);　SSIM　(0.86)).　The　approach　that　has　been　described　would　make　it　easier　to　apply　future　models　for　MRI　reconstruction　without　necessitating　the　acquisition　of　vast　imaging　datasets.