Personalized　federated　learning　(PFL)　addresses　the　significant　challenge　of　non-independent　and　identically　distributed　(non-IID)　data　across　clients　in　federated　learning　(FL).　Our　proposed　framework,　＂FairDPFL-SCS:　Fair　Dynamic　Personalized　Federated　Learning　with　Strategic　Client　Selection＂,　marks　a　notable　advancement　in　this　domain.　By　integrating　dynamic　learning　rate　adjustments　and　a　strategic　client　selection　mechanism,　our　approach　effectively　mitigates　the　challenges　posed　by　non-IID　data　while　enhancing　model　personalization,　fairness,　and　efficiency.　We　evaluated　FairDPFL-SCS　using　standard　datasets,　including　MNIST,　FashionMNIST,　and　SVHN,　employing　architectures　like　VGG　and　CNN.　Our　model　achieved　impressive　results,　attaining　99.04%　accuracy　on　MNIST,　89.19%　on　FashionMNIST,　and　90.9%　on　SVHN.　These　results　represent　a　substantial　improvement　over　existing　methods,　including　a　highest　increase　of　16.74%　inaccuracy　on　SVHN　when　compared　to　the　best-performing　benchmark　methods.　In　particular,　our　method　also　demonstrated　lower　fairness　variance,　presenting　the　importance　of　fairness　in　model　personalization,　a　frequently　overlooked　aspect　in　FL　research.　Through　extensive　experiments,　we　validate　the　superior　performance　of　FairDPFL-SCS　compared　to　benchmark　PFL　approaches,　highlighting　significant　improvements　over　state-of-the-art　methods.　This　work　represents　a　promising　step　forward　in　the　field　of　federated　learning,　offering　a　comprehensive　solution　to　the　challenges　presented　by　non-IID　data　while　prioritizing　fairness　and　efficiency　in　model　personalization.