Healthcare systems worldwide face mounting pressure to deliver better outcomes with limited resources, making clinical decision support tools increasingly critical for frontline nurses. A comprehensive analysis of AI-powered nursing interventions reveals measurable improvements across multiple care domains, suggesting these technologies could fundamentally reshape how nurses assess and respond to patient needs. The systematic review examined eight experimental studies implementing AI decision support systems in nursing practice between 2005 and 2024. Key findings demonstrate substantial clinical improvements: discharge planning algorithms reduced 30-day hospital readmissions from 22.2% to 9.4%, while patient deterioration detection systems accelerated critical communications with senior staff and diagnostic test ordering. Neonatal resuscitation accuracy jumped dramatically from baseline ranges of 55-80% to 94-95% with AI guidance, and nurses showed significantly improved confidence in seizure assessments. Additional benefits included enhanced pressure ulcer prevention protocols and more accurate visual diagnostic differentiation. This evidence base represents a crucial validation point for AI integration in nursing practice. Unlike previous technology adoptions that often created additional documentation burdens, these AI tools appear to enhance rather than complicate clinical workflows. The improvements span both acute interventions and preventive care, suggesting broad applicability across nursing specialties. However, the limited number of high-quality studies and relatively short implementation periods warrant cautious interpretation. Most trials focused on specific clinical scenarios rather than comprehensive workflow integration, leaving questions about scalability and long-term adoption rates. The nursing profession's historically skeptical approach to technology adoption may also influence real-world implementation success beyond these controlled research environments.