Based on and inspired by the approach of human-centered AI (HCAI), intercultural human-centered AI (IHCAI) examines how AI systems are designed, developed, and deployed to respect cultural diversity and serve global user populations. This chapter synthesizes foundational concepts and cultural models to explain how expectations about communication, decision-making, and interaction shape human–AI experiences. Building on these foundations, it translates concepts into actionable methods for design and evaluation, including localization, culturally adaptive interfaces, multilingual interaction, and participatory design with diverse stakeholders. The chapter addresses critical challenges in global deployment such as dataset bias, conflicting ethical norms across regions, and the balance between standardization and localization. It also discusses ethics, fairness, and cross-cultural governance. It analyzes opportunities and risks in large language models, including bias, unequal language performance, culturally aware explainability, and adaptation. Applications across healthcare, education, and business illustrate how culturally sensitive design improves acceptance and effectiveness. The chapter concludes with guiding principles, success factors, and metrics for usability, trust, inclusion, and cultural appropriateness. It proposes a roadmap for scalable cultural intelligence, real-time adaptation, and evidence-based evaluation equipping researchers, practitioners, and policymakers with a coherent framework and practical guidance to create AI that is globally usable, locally appropriate, and aligned with diverse values.