Large Language Models (LLMs) are transforming industries by enabling advanced natural language processing (NLP) applications, yet their adoption raises ethical and regulatory concerns. Bias in training data can lead to unfair outcomes in hiring, credit scoring, and legal decisions, necessitating bias mitigation techniques such as debiasing algorithms and diverse data curation. Privacy risks arise from LLMs memorizing sensitive data, requiring compliance with regulations like GDPR and CCPA through differential privacy and encryption-based training. The opacity of LLMs challenges trust, prompting research in Explainable AI (XAI) methods like SHAP and LIME. Governments and industry leaders are shaping AI regulations, such as the EU AI Act, to balance innovation with responsibility. Future AI governance will require interdisciplinary collaboration, ethical auditing, and continuous oversight. Ensuring fairness, transparency, and accountability will be key to responsible AI deployment, enabling organizations to leverage LLMs’ potential while minimizing risks.