Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a long preclinical phase and a cascade of pathological events. With recent advances in blood and imaging biomarkers, the dynamic tracking of AD progression has become increasingly feasible. However, most studies have focused on single-modality markers, with limited integration across biological systems and time scales. This review adopts a time-dynamic perspective to summarize the longitudinal evolution of key blood biomarkers (e.g., Aβ42/40 ratio, p-tau217, NfL, GFAP) and imaging markers (e.g., Aβ-PET, Tau-PET, MRI) across the AD continuum. We compare the timing of initial abnormalities, progression patterns, and correlations with cognitive decline. Furthermore, we highlight integrative modeling approaches, including event-based models, and deep learning frameworks, which enable multi-modal risk prediction and individualized disease staging. The clinical potential of such integration in early screening, disease trajectory forecasting, and therapeutic decision-making is discussed, along with current limitations and future directions. This review provides a framework for developing dynamic, biology-driven precision diagnostic strategies in AD.