Background <p>Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer’s disease (AD), encompassing both conventional statistical methods and deep learning techniques.</p> Methods <p>Articles were sourced from PubMed, Embase and Web of Science databases using keywords related to dynamic prediction of AD. A set of criteria was developed to identify included studies. The correlation information for the construction of models was extracted.</p> Results <p>We identified four methodological frameworks for dynamic prediction from 18 studies with two-stage model (<i>n</i> = 3), joint model (<i>n</i> = 11), landmark model (<i>n</i> = 2) and deep learning (<i>n</i> = 2). We reported and summarized the specific construction of models and their applications.</p> Conclusions <p>Each framework possesses distinctive principles and attendant benefits. The dynamic prediction models excel in predicting the prognosis of individual patients in a real-time manner, surpassing the limitations of traditional baseline-only prediction models. Future work should consider various data types, complex longitudinal data, missing data, assumption violations, survival outcomes, and interpretability of models.</p>