In an era where data is both an asset and a liability, the integration of artificial intelligence (AI) and machine learning (ML) into data governance frameworks has emerged as a transformative approach. Learning-driven data fabric governance leverages AI/ML to enable dynamic, scalable, and intelligent management of distributed data assets across hybrid and multi-cloud environments. This chapter explores how learning-enabled data fabrics can ensure compliance with global data standards and regulatory requirements, such as GDPR, CCPA, HIPAA, and others, by automating data classification, lineage tracking, and policy enforcement. The chapter presents the architecture of an AI-augmented data fabric that continuously learns from data access patterns, governance rule changes, and compliance metrics to optimize policies and controls. It also addresses challenges related to data quality, metadata management, and ethical AI in regulatory compliance. By embedding learning mechanisms into the data governance layer, organizations can respond proactively to changing regulatory landscapes and reduce manual oversight. This chapter further explores real-world use cases from regulated industries such as finance, health care, and telecommunications, demonstrating how a learning-driven approach ensures audit readiness and transparency. Emphasis is placed on aligning technical infrastructure with legal and ethical standards through AI-enhanced automation. By bridging the gap between data science and compliance disciplines, learning-driven governance offers a sustainable model for maintaining regulatory trust and data stewardship at scale.