Artificial Intelligence (AI) is increasingly being integrated into pediatric emergency medicine, offering new tools and approaches for clinical decision-making. This review explores the theoretical foundations of AI, its applications in pediatric emergencies, and the challenges that hinder its widespread adoption. AI models have shown superior performance in areas such as triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. These models outperform traditional methods by analyzing large datasets and identifying patterns that may be missed by conventional approaches. However, the implementation of AI in healthcare faces several challenges. These include technological limitations, ethical concerns, age-related differences in data interpretation, and the lack of comprehensive datasets in the pediatric context. Future research should focus on validating AI models using prospective datasets with larger sample sizes and tailoring algorithms to specific medical needs. Collaboration between clinicians and developers is essential to ensure that AI tools are effective and safe. AI also has potential applications in stress management for pediatric patients, such as AI-enhanced socially assistive robots that can provide emotional support during painful procedures. These robots can help reduce distress and improve patient outcomes by offering interactive, personalized support. In the context of traumatic brain injury assessment, AI has been used to develop predictive models that can identify patients at risk of severe brain injury. These models have shown high accuracy in predicting outcomes and can help reduce unnecessary CT scans while maintaining sensitivity for identifying clinically significant injuries. For sepsis prediction, AI models have demonstrated the ability to detect early signs of sepsis in pediatric patients, allowing for timely interventions. These models can improve the accuracy of sepsis detection and help reduce mortality rates. Despite these advancements, challenges remain in ensuring the reliability and ethical use of AI in healthcare. Issues such as data privacy, bias, and the need for transparent decision-making processes must be addressed to build trust in AI tools. Future research should focus on improving the generalization of AI models and integrating them into clinical workflows to enhance patient care.Artificial Intelligence (AI) is increasingly being integrated into pediatric emergency medicine, offering new tools and approaches for clinical decision-making. This review explores the theoretical foundations of AI, its applications in pediatric emergencies, and the challenges that hinder its widespread adoption. AI models have shown superior performance in areas such as triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. These models outperform traditional methods by analyzing large datasets and identifying patterns that may be missed by conventional approaches. However, the implementation of AI in healthcare faces several challenges. These include technological limitations, ethical concerns, age-related differences in data interpretation, and the lack of comprehensive datasets in the pediatric context. Future research should focus on validating AI models using prospective datasets with larger sample sizes and tailoring algorithms to specific medical needs. Collaboration between clinicians and developers is essential to ensure that AI tools are effective and safe. AI also has potential applications in stress management for pediatric patients, such as AI-enhanced socially assistive robots that can provide emotional support during painful procedures. These robots can help reduce distress and improve patient outcomes by offering interactive, personalized support. In the context of traumatic brain injury assessment, AI has been used to develop predictive models that can identify patients at risk of severe brain injury. These models have shown high accuracy in predicting outcomes and can help reduce unnecessary CT scans while maintaining sensitivity for identifying clinically significant injuries. For sepsis prediction, AI models have demonstrated the ability to detect early signs of sepsis in pediatric patients, allowing for timely interventions. These models can improve the accuracy of sepsis detection and help reduce mortality rates. Despite these advancements, challenges remain in ensuring the reliability and ethical use of AI in healthcare. Issues such as data privacy, bias, and the need for transparent decision-making processes must be addressed to build trust in AI tools. Future research should focus on improving the generalization of AI models and integrating them into clinical workflows to enhance patient care.