Healthcare systems struggling with nursing shortages and complex patient needs may have found a powerful ally in artificial intelligence. Analysis of AI implementation across nursing workflows reveals measurable improvements in patient outcomes and clinical efficiency that could reshape bedside care delivery. Eight validated studies demonstrate that AI-powered decision support systems delivered substantial gains across critical nursing functions. A discharge planning algorithm reduced 30-day hospital readmissions from 22% to just 9.4%, while deterioration detection tools accelerated response times for contacting senior staff and ordering diagnostic tests. Perhaps most striking, AI-assisted neonatal resuscitation training elevated accuracy rates to 94-95%, compared to conventional methods achieving only 55-80% success rates. The technology also enhanced seizure assessment confidence, pressure ulcer prevention protocols, and documentation quality—all statistically significant improvements. This systematic review captures a pivotal moment as nursing practice intersects with machine learning capabilities. The findings suggest AI excels at pattern recognition tasks that complement human clinical judgment rather than replacing it. However, the evidence base remains limited to eight experimental studies, and real-world implementation faces obstacles including workflow integration, training requirements, and ensuring algorithms perform equitably across diverse patient populations. While promising, these early results represent controlled environments rather than the chaotic realities of understaffed units. The technology's true test will be whether it can maintain these performance gains when deployed at scale across varied healthcare settings and nursing expertise levels.