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Novel EEG-Based Machine Learning Framework for Early Detection of Chronic Kidney Disease Abstract The early detection of Chronic Kidney Disease (CKD) remains a clinical challenge, particularly in its asymptomatic initial stages. Given the increasingly recognized connection between CKD and neural dysfunction, this study proposes a novel electroencephalogram (EEG)-based machine learning framework for early CKD detection. By leveraging advanced time-frequency analysis and state-of-the-art machine learning techniques, the present approach aims to identify subtle EEG abnormalities associated with early-stage CKD, reducing diagnostic delay and potentially enabling preemptive intervention. 1. Introduction Chronic Kidney Disease (CKD) is a global health problem marked by gradual and irreversible loss of kidney function, often undiagnosed until advanced stages. Neurological dysfunction is prevalent even in early CKD due to accumulating uremic toxins affecting CNS function, manifesting as subtle