Surgical site infections (SSIs) remain a major healthcare challenge, affecting patient outcomes and increasing operational costs. Although artificial intelligence (AI) shows potential for improving the prediction, detection, and prevention of SSIs, the literature lacks a comprehensive systems design framework to support its implementation. This study addresses this gap by developing an enhanced systems-thinking, AI-driven decision support framework that integrates design-thinking principles. The framework comprises four components: people, systems, design, and risks. Through a review of existing literature and current practices, the study identifies key factors influencing AI implementation in SSI management, including user-centered design, system interoperability, iterative prototyping, and risk assessment. The resulting framework provides a structured approach for integrating AI into SSI management and offers guidance for researchers, clinicians, and healthcare administrators. The study concludes with recommendations for applying the framework in healthcare settings, emphasizing interdisciplinary collaboration, stakeholder engagement, and proactive risk management to improve patient safety and reduce healthcare-associated infections.