Neurodegenerative diseases such as Alzheimer’s and Parkinson’s impose a staggering global burden, yet timely identification remains hindered by a fundamental mismatch between the slow unfolding of pathology and the static nature of traditional diagnostic frameworks. While conventional clinical markers often fail to identify decline until irreversible neuronal loss has occurred, artificial intelligence (AI)-driven biomarkers derived from neuroimaging, electrophysiology, and digital phenotyping offer a transformative proactive paradigm. This review evaluates how machine-learning models extract high- dimensional, subvisual patterns from MRI, PET, and EEG datasets to detect preclinical deviations that outpace traditional markers in predictive timelines. We argue that the primary value of these technologies lies in a categorical shift toward continuous, temporally informed disease modeling designed to fill the "detection gap" between early protein accumulation and overt clinical impairment. By synthesizing evidence across various modalities, we highlight the superior performance of multimodal fusion architectures in capturing the biological complexity of neurodegeneration. However, clinical translation faces significant hurdles, including data heterogeneity, the "black-box" nature of deep learning, and the necessity for global equity in dataset representation. Ultimately, by integrating explainable AI with longitudinal data streams, these biomarkers can redefine neurodegenerative care-transforming diagnosis from a reactive confirmation of damage into a precise tool for risk stratification, trial enrichment, and early therapeutic intervention.