Objective: Artificial intelligence (AI) is increasingly used in childhood and adolescent obesity management, including risk prediction, dietary assessment, behavioral monitoring, personalized intervention, and digital therapeutics. This narrative review aims to synthesize current evidence and critically evaluate its methodological robustness, practical relevance, ethical challenges, and future directions. Methods: Literature was searched in PubMed, CNKI, and Web of Science using terms related to AI, obesity, and pediatric populations. Evidence from systematic reviews, randomized controlled trials, validated prediction models, and representative digital health interventions was prioritized. The findings were synthesized using an AI-enabled prevention–monitoring–intervention closed-loop framework. Results: Current evidence suggests that machine learning and deep learning can support early obesity risk prediction; computer vision may improve dietary assessment; wearable devices and Internet of Things systems enable continuous behavioral monitoring; and recommendation systems, exergaming, gamification, and digital therapeutics may promote personalized behavior change. However, the evidence remains heterogeneous, with limitations including small or single-center datasets, short follow-up periods, insufficient external validation, inconsistent outcome measures, algorithmic bias, privacy concerns, and uncertain real-world applicability. Conclusion: AI has the potential to shift childhood obesity management from episodic and uniform care toward proactive, adaptive, and personalized support. Future research should prioritize robust validation, privacy-preserving data governance, fairness, long-term effectiveness, and human-in-the-loop implementation across family, school, community, and healthcare settings.

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