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Early identification of individuals at risk of cognitive decline, particularly those with mild cognitive impairment, remains a major clinical and scientific challenge. In this context, artificial intelligence has emerged as a promising framework for prognostic modeling and for the identification of clinically relevant predictors of disease onset and progression. This narrative review provides a theoretical and historical synthesis of the main machine learning and deep learning approaches used to predict cognitive decline and dementia from multimodal data, including biomarkers, clinical and demographic variables, cognitive measures, and neuroimaging markers. The review examines the methodological evolution of the field, from traditional statistical approaches to ensemble methods and deep neural architectures, and discusses the growing role of multimodal and longitudinal data integration. 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