Background: Electroencephalography (EEG) has emerged as a minimally invasive technique to quantify functional changes in neural activity associated with neurodegenerative disorders such as Alzheimer’s Disease (AD). Given its non-invasive approach, EEG has the potential to fill the pressing gap forearly, accurate, and accessible methods to detect and characterize disease progression in AD. Methods: To address these challenges, we conducted a pilot analysis of a custom machine learning-based automated preprocessing and feature extraction pipeline to identify indicators of AD and correlates of disease progression. Results: Our pipeline successfully detected several new and previously established EEG-based measures indicative of AD status and progression. Key findings included alterations in delta and theta band power, network connectivity disruptions, and increased slowing of brain rhythms. Additionally, we observed strong correlations between EEG-derived metrics and clinical measures such as Mini-Mental State Examination (MMSE) scores, supporting the external validity of our approach. These findings highlight the sensitivity of EEG biomarkers in differentiating between early and late stages of AD. Conclusions: Our findings suggest that this automated approach provides a promising initial framework for implementing EEG biomarkers in the AD patient population, paving the way for improved diagnostic and monitoring strategies.