Purpose of Review <p>Artificial Intelligence (AI) and Machine Learning (ML) are rapidly evolving fields with growing implications for pediatric healthcare, education, and research. This review synthesizes current evidence, highlights key applications across pediatric subspecialties, and outlines limitations and future directions. It explores how AI tools that range from diagnostic models to large language models (LLMs) are being leveraged in pediatric clinical practice, medical education, and research, while emphasizing the need for careful integration, validation, and oversight.</p> Recent Findings <p>AI applications in pediatrics include diagnostic support, prognostic modeling, risk prediction, therapeutic planning, treatment monitoring, and clinical decision support. In specialties like endocrinology, neurology, and emergency medicine, AI has demonstrated potential to enhance early diagnosis, optimize resource use, and reduce errors. LLMs are increasingly used for personalized feedback, curriculum development, and patient communication. In research, AI is enabling new insights through natural language processing and advanced predictive modeling. However, challenges persist, including variability in model performance, data bias, ethical and medicolegal concerns, and limitations in transparency (‘black box’ problem). LLMs also pose risks of inconsistency, misinformation, and over-reliance by users. Pediatric-specific governance frameworks and professional education programs, such as those led by the American Academy of Pediatrics, are emerging to address these issues.</p> Summary <p>AI and ML hold significant promise for transforming pediatric care, education, and research. However, realizing this potential requires rigorous clinical validation, thoughtful implementation, and strong ethical safeguards. Pediatricians must remain engaged in AI development and evaluation to ensure these tools enhance, rather than replace, clinical judgment. Educating clinicians about responsible AI use, improving data quality and equity, and embedding tools into clinical workflows are key priorities. Generative AI, particularly LLMs, should be embraced cautiously, with clear oversight, stakeholder input, and ongoing professional development to ensure alignment with the unique needs of pediatric populations.</p>