| Sumario: | Obesity remains one of the most urgent global public health challenges, necessitating innovative and scalable strategies for effective weight management. This narrative review aims to synthesize current evidence (2021-2026) on the role of artificial intelligence (AI) in weight loss and obesity management, and to evaluate its clinical potential, limitations, and future directions. Recent advances in AI, including machine learning and deep learning techniques, have introduced novel opportunities for personalized nutrition, predictive modeling, and digitally supported behavioral interventions. The literature indicates that AI-driven systems show substantial potential in predictive weight loss modeling, reinforcement learning-based treatment optimization, digital coaching platforms, and biomarker-integrated personalization strategies. Importantly, while AI technologies may enhance scalability and personalization, they should be positioned as clinical decision-support tools rather than replacements for dietitians and healthcare professionals. However, the field remains heterogeneous, with a limited number of long-term randomized controlled trials, variable methodological transparency, and insufficient external validation of predictive models. While AI technologies may enhance scalability and personalization, they should be positioned as clinical decision-support tools rather than replacements for dietitians and healthcare professionals. Ethical considerations, data governance, and algorithmic transparency remain critical for safe and responsible implementation. Overall, AI represents a promising adjunct in weight management; however, its integration into clinical nutrition practice requires rigorous validation and interdisciplinary collaboration.
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