This chapter explores the evolving landscape of AI governance, emphasizing the integration of ethical reasoning, legal responsibility, and institutional accountability beyond regulatory compliance. The proposed conceptual framework outlines four core dimensions of AI governance—normative, institutional, technical, and contextual—which collectively support the responsible design, deployment, and oversight of AI systems. A comparative analysis of global regulatory models, including the EU AI Act and frameworks from the U.S., China, and Latin America, reveals differing approaches to risk, transparency, and enforceability. The chapter also introduces key ethical principles—such as fairness, transparency, autonomy, and accountability—and their translation into technical mechanisms, including bias mitigation tools, explainability methods, model documentation, and continuous oversight. By institutionalizing governance through ethical committees, legal instruments, and training protocols, the chapter argues for a holistic and adaptive model grounded in trust, human rights, and democratic values. Finally, the text underscores the importance of global coordination and the need for interoperable standards that balance universal ethical commitments with local contextualization. This multifaceted approach positions AI governance as a foundational pillar for equitable and trustworthy technological innovation.