Chronic wounds pose significant global health challenges due to their prevalence, economic burden, and substantial impact on patient quality of life. Artificial intelligence (AI) presents transformative opportunities in personalized wound care by leveraging diverse datasets, including medical imaging, electronic health records, wearable sensors, and laboratory biomarkers. This chapter reviews the key data sources for AI applications and explores current AI-driven assessment techniques, such as predictive analytics, risk stratification, computer vision tools, and multimodal data fusion, along with rule-based and data-driven therapeutic strategies. Additionally, a multimodal agentic AI framework is proposed to autonomously integrate diverse patient data, perform comprehensive wound assessments, and dynamically customize treatment plans based on continuous clinical feedback. Lastly, the ethical, regulatory, economic, and policy dimensions of deploying AI technologies, emphasizing data privacy, bias mitigation, and interoperability, are also discussed.