Magnetic　Resonance　Imaging　(MRI)　is　essential　for　high-resolution　soft-tissue　imaging　but　suffers　from　long　acquisition　times,　limiting　its　clinical　efficiency.　Accelerating　MRI　through　undersampling　k-space　data　leads　to　ill-posed　inverse　problems,　introducing　noise　and　artifacts　that　degrade　image　quality.　Conventional　deep　learning　models,　including　conditional　and　unconditional　approaches,　often　face　challenges　in　generalization,　particularly　with　variations　in　imaging　operators　or　domain　shifts.　In　this　study,　we　propose　PINN-DADif,　a　Physics-Informed　Neural　Network　integrated　with　deep　adaptive　diffusion　priors,　to　address　these　challenges　in　MRI　reconstruction.　PINN-DADif　employs　a　two-phase　inference　strategy:　an　initial　rapid-diffusion　phase　for　fast　preliminary　reconstructions,　followed　by　an　adaptive　phase　where　the　diffusion　prior　is　refined　to　ensure　consistency　with　MRI　physics　and　data　fidelity.　The　inclusion　of　physics-based　regularization　through　PINNs　enhances　the　model＇s　adherence　to　k-space　constraints　and　gradient　smoothness,　leading　to　more　accurate　reconstructions.　This　adaptive　approach　reduces　the　number　of　iterations　required　compared　to　traditional　diffusion　models,　improving　both　speed　and　image　quality.　We　validated　PINN-DADif　on　a　private　MRI　dataset　and　the　public　fastMRI　dataset,　where　it　outperformed　state-of-the-art　methods.　The　model　achieved　PSNR　values　of　41.2,　39.5,　and　41.5,　and　SSIM　values　of　98.7,　98.0,　and　98.5　for　T1,　T2,　and　Proton　Density-weighted　images　at　R　=　4x　on　the　private　dataset.　Similar　high　performance　was　observed　on　the　fastMRI　dataset,　even　in　scenarios　involving　domain　shifts.　PINN-DADif　marks　a　significant　advancement　in　MRI　reconstruction　by　providing　an　efficient,　adaptive,　and　physics-informed　solution.