Alzheimer's disease (AD) involves proteostasis dysregulation causing protein misfolding, but whether these structural changes manifest as plasma conformational biomarkers remains unclear. We profiled plasma protein structures from 520 participants including individuals with AD, individuals with mild cognitive impairment (MCI) and healthy controls. Using mass spectrometry and machine learning, we systematically characterized the structural proteome changes associated with ApoE variations and neuropsychiatric symptoms to identify AD-specific signatures. We developed a diagnostic panel using peptides from C1QA, CLUS and ApoB representing AD-associated structural changes. This three-marker panel achieved 83.44% accuracy in three-way classification (healthy versus MCI versus AD). Binary classification yielded area under the receiver operating characteristic curves of 0.9343 for healthy versus MCI and 0.9325 for MCI versus AD. Longitudinal samples were classified with 86.0% accuracy. This multi-marker panel based on plasma protein structural alterations represents a promising diagnostic approach that may enhance early AD detection and provide insights for clinical trials, improving therapeutic outcomes.