As Artificial Intelligence (AI) technologies become increasingly embedded in critical aspects of modern life—ranging from healthcare diagnostics and financial forecasting to autonomous vehicles, law enforcement, education, and national security—the urgency of addressing their ethical implications has grown exponentially. While AI systems offer unprecedented efficiencies and capabilities, they also present significant risks, including algorithmic bias, opaque decisionmaking processes, data exploitation, invasion of privacy, digital surveillance, job displacement, and the amplification of societal inequalities. These risks are particularly acute in high-stakes domains where errors or unchecked use can result in irreversible harm or systemic injustice. This paper offers a comprehensive examination of the evolving ethical landscape surrounding AI development and deployment. It explores foundational ethical principles such as fairness, accountability, transparency, and human-centered design, alongside contemporary challenges introduced by machine learning models, deep learning algorithms, and autonomous decision systems. Special attention is given to the global regulatory landscape, comparing initiatives such as the European Union’s AI Act, the U.S. Blueprint for an AI Bill of Rights, and guidelines from organizations like UNESCO and the OECD. The paper also examines the growing role of interdisciplinary AI ethics teams, algorithmic auditing, and impact assessments. Ultimately, the paper proposes a strategic roadmap for building ethical AI ecosystems grounded in inclusivity, explainability, legal compliance, and social well-being. It emphasizes that aligning AI development with democratic values, human dignity, and global equity is not merely desirable— but essential—for ensuring that the future of AI serves humanity as a whole, rather than a privileged few.