In the age of exponential data growth and real-time decision-making requirements, conventional data structures cannot scale, are latency-constrained, and incapable of consolidating data. The chapter introduces learning-driven data fabric (LDDF), an AI-powered, disruptive data structure that consumes, consolidates, processes, and analyzes heterogeneous data independently in distributed systems. With machine learning (ML) and real-time analytics, LDDF offers a self-optimizing and adaptive platform that breaks data silos and produces predictive insights across industries from healthcare to finance to smart cities. The chapter delves into the evolutionary heritage of data management systems, the architectural building blocks of LDDF, and the real-time processing technologies that fuel it—Apache Flink, Spark Streaming, and Google Dataflow. AI-powered features such as automated data governance, anomaly detection, and context-aware querying are thoroughly examined. Case studies highlight the applications of LDDF in fraud detection, early disease detection, and smart traffic management. The chapter also reveals emerging trends such as federated learning (FL), blockchain, and quantum computing (QC) and discusses significant challenges in AI bias, ethics-driven decision-making, and interoperability. Finally, LDDF is introduced as a paradigm for Intelligent, secure, and scalable data management in today’s digital era.