The phenotypic and functional changes of cells in response to physiological and pathological conditions are strongly influenced by the roles of plasma membrane proteins. Recombinant Fab antibody-based phage display for an unbiased antigen-driven affinity selection is a suitable approach for identifying novel membrane proteins. Alterations in the function and distribution of cell membrane proteins in pancreatic beta cells have been observed in pathological conditions like diabetes. In this study, we integrated an unbiased cell-based Fab-phage display screening method with bioinformatics tools to identify and characterize Fabs that selectively bind to pancreatic beta cells in conditions simulated by hyperglycemic environment. We isolated three Fab-phages, namely Fab_53, 538, and 54.68, that have binding properties matching specific epitopes on the MIN6 membrane. These Fabs are part of the immunoglobulin G groups that contain Kappa light chains. Bioinformatics analysis of the variable domains of their light and heavy chains (VL and VH) revealed that the potential epitopes binding sites on the beta cell membrane are associated with pathways involved in insulin activity. Through FACS and IF analysis, we found that, of the three Fabs, Fab_538 exhibited the strongest binding to MIN6 cells. The use of InterProScan software resulted in the generation of 344 potential Fab_538 binding epitopes, and from these, AlphaFold predicted 10 interacting antigens. Our goal in combining Fab-phage display with bioinformatic tools is to develop a more effective, specific, and streamlined method for identifying disease-modifying membrane epitopes for monoclonal antibodies (mAbs).