Artificial intelligence (AI) and machine learning (ML) are rapidly transforming neonatal intensive care by enabling predictive analytics from large volumes of clinical data. This systematic review evaluated 68 published studies examining AI and ML applications for predicting neonatal morbidities, mortality, and length of stay in NICUs. The review analyzed prediction models, data sources, machine learning techniques, and performance metrics while identifying current limitations and future opportunities. The authors propose that integrating multimodal data—including electronic medical records, physiologic monitoring, medical imaging, laboratory results, and environmental sensing—can enable next-generation clinical decision support systems that improve neonatal outcomes through personalized, real-time predictive analytics.