Abstract

Brain aging is a complex and heterogeneous process associated with progressive cognitive changes and an increased risk of neurodegenerative diseases, such as Alzheimer's disease. In recent decades, artificial intelligence (AI) has emerged as a strategic tool for understanding, early detection, and management of these conditions. This systematic review, conducted in accordance with the PRISMA 2020 guidelines, aimed to analyze the applications of AI in brain aging, focusing on the early detection of cognitive decline, the relationship with neuroplasticity, and the use of digital technologies in clinical and preventive contexts. The included studies demonstrate that machine learning and deep learning algorithms are widely used in the analysis of neuroimaging, EEG, and multimodal clinical data, allowing the identification of early patterns of cognitive risk. In addition, AI has been integrated into cognitive, physical, and combined interventions, as well as wearable devices, neurotechnologies, and brain-computer interfaces, contributing to personalized strategies and the promotion of healthy aging. Despite advances, challenges related to methodological standardization, clinical validation, and ethical issues remain relevant. It is concluded that artificial intelligence represents a promising resource in the care of brain aging, provided it is applied in an ethical, integrated, and evidence-based manner.