Integration of artificial intelligence (AI) has brought about transformative possibilities in pediatric acute care wherein multimodal approaches leveraging diverse data types can enhance diagnostic accuracy and treatment decisions. Diverse forms of patient data including vitals, clinical observations, lab values, and medical imaging can be collated through multimodal AI to create comprehensive and astute predictive models. The generation of integrated clinical decision support tools using AI is particularly useful in time sensitive settings of pediatric emergency and critical care where early identification and timely intervention can significantly affect the eventual outcomes. Additionally, use of AI in pediatric critical care has the potential of streamlining workflows for improved efficiency and enhancing patient safety through minimizing medication errors. Early trends emerging from application of multimodal AI in pediatric critical care in conditions such as triage, sepsis, traumatic brain injury, and necrotizing enterocolitis have shown efficacy exceeding traditional clinical methods and tools. This early promise comes with challenges such as data quality, technical intricacies, real-world implementation, ethical issues, and regulatory considerations. The path forward involves addressing current limitations through interprofessional collaborations, data enhancement, strategies, improvement in AI models, prospective clinical validation, ethical frameworks, and regulatory guidance.