The deposition of amyloid-β (Aβ) protein in the human brain is a hallmark of Alzheimer's disease and is related to cognitive decline. However, the relationship between early Aβ deposition and future cognitive impairment remains poorly understood, particularly concerning its spatial distribution and network-level effects. Here, we employed a cross-validated machine learning approach and investigated whether integrating subject-specific brain connectome information with Aβ burden measures improves predictive validity for subsequent cognitive decline. Baseline regional Aβ pathology measures from positron emission tomography (PET) imaging predicted prospective cognitive decline. Incorporating structural connectome, but not functional connectome, information into the Aβ measures improved predictive performance. We further identified a neuropathological signature pattern linked to future cognitive decline, which was validated in an independent cohort. These findings advance our understanding of how Aβ pathology relates to brain networks and highlight the potential of network-based metrics for Aβ-PET imaging to identify individuals at higher risk of cognitive decline.