Glycation is a non-enzymatic post-translational modification that leads to the formation of advanced glycation end-products (AGEs), which accumulate in the bloodstream under chronic hyperglycemia and are implicated in diabetes-related pathologies. While glycated proteins such as albumin or hemoglobin are widely used as biomarkers for glycemic control, the structural and chemical changes induced by glycation may also alter their interactions with lipid interfaces, including cellular membranes and lipoproteins, potentially affecting their biological distribution and diagnostic detectability. In this study, we investigated how glycation influences the interaction of bovine serum albumin (BSA) with supported lipid bilayers (SLBs) of different compositions, used as model systems to replicate the diversity of membrane surface charges and fluidity. Using neutron reflectometry (NR), we compared the membrane association of BSA and a chemically-enhanced glycated form of BSA (gBSA), focusing on nanostructural changes at the bilayer interface. Our results showed negligible interaction of either proteins with zwitterionic or cationic membranes. In contrast, both BSA and gBSA exhibited significant binding to negatively charged bilayers, with glycation significantly amplifying this interaction. Quantitatively, the membrane-associated protein volume fraction increased from 0.11 (BSA) to 0.17 (gBSA), suggesting that glycation modifies the protein’s surface properties in ways that promote stronger lipid interactions with negatively charged membranes. These findings suggest that glycation not only affects protein structure but also modulates protein–membrane affinity in a lipid-dependent manner. This has important implications for the bioavailability and behavior of glycated albumin in the bloodstream, potentially influencing the accuracy of clinical assays and contributing to membrane-related pathophysiology in diabetes. Our work highlights the need for a deeper understanding of glycation-induced changes in protein-lipid interactions and their consequences for biomarker reliability and disease mechanisms.