Patient-centered nursing practices prioritize individualized care, shared decision-making, and holistic treatment approaches that align with each patient’s unique health needs, preferences, and circumstances. However, delivering highly personalized care in dynamic healthcare environments remains challenging due to increasing patient volumes, complex comorbidities, and the rapid evolution of medical evidence. This paper explores the integration of artificial intelligence (AI)-driven clinical decision support systems (CDSS) with personalized care planning to enhance nursing practice and improve patient outcomes. AI-powered CDSS leverage machine learning algorithms, predictive analytics, and natural language processing to analyze patient health records, laboratory results, imaging data, and real-time monitoring streams, providing nurses with evidence-based recommendations tailored to individual patient profiles. By embedding AI insights directly into nursing workflows, care teams can anticipate patient needs, identify early signs of clinical deterioration, and adjust interventions in real time. Personalized care plans are dynamically updated through continuous data integration, ensuring that interventions remain relevant, patient-centered, and outcome-focused. These systems support nurses in prioritizing high-risk patients, optimizing resource allocation, and reducing variability in care delivery. Case study simulations in acute care settings indicate that combining AI-driven decision support with individualized nursing care plans can improve adherence to clinical guidelines, reduce preventable complications, and enhance patient satisfaction scores. Ethical considerations such as algorithm transparency, bias mitigation, and patient data privacy are addressed to ensure equitable and trustworthy care. The findings suggest that AI-augmented nursing practices represent a transformative step toward scalable, high-quality, patient-centered healthcare models capable of adapting to evolving clinical and patient needs.