Knowledge-intensive firms increasingly rely on data-driven decision systems to manage complex processes, enhance productivity, and support innovation. However, as firms generate massive volumes of operational, transactional, and behavioral metadata, the ability to harness this meta-level information becomes crucial. This paper introduces a comprehensive framework for Meta-Data Driven Decision Models (MDDDMs) in knowledge-intensive environments, discussing metadata categories, modeling techniques, system architectures, decision workflows, and organizational impacts. Using theoretical analysis and published case insights, the study proposes a meta-data decision pipeline that integrates semantic modeling, AI reasoning, and organizational knowledge networks. The implications for decision quality, knowledge retention, automation, and competitive advantage are evaluated. Future research directions for hybrid human-AI meta-data decision ecosystems are also outlined.