<![CDATA[This research proposes a Unified Artificial Intelligence–Driven Data Governance Framework to enhance decision intelligence in smart digital ecosystems. The rapid growth of technologies such as the Internet of Things (IoT), smart cities, and digital platforms has led to an exponential increase in data volume and complexity, creating challenges related to data silos, poor data quality, lack of governance standards, and ineffective decision-making. While artificial intelligence (AI) has been widely adopted to address analytical needs, existing approaches often fail to integrate data governance with AI-driven decision processes, resulting in unreliable and less transparent outcomes. To address this gap, this study develops a multi-layered framework that integrates data governance, AI, and decision intelligence into a unified architecture. The proposed framework consists of a data layer, governance layer, AI layer, decision layer, and application layer, supported by key components such as data integration modules, data quality engines, policy enforcement mechanisms, AI model management, and decision support systems. A prototype-based methodology is employed to evaluate the framework using machine learning models and optimization techniques within simulated smart ecosystem environments. The results demonstrate that the proposed framework significantly improves decision accuracy, data quality, and system reliability while maintaining acceptable processing time and scalability. Compared to traditional systems and non-governed AI models, the framework provides enhanced transparency, accountability, and compliance. However, challenges related to computational cost, system complexity, scalability, and ethical considerations such as bias and fairness remain. This research contributes to the field by presenting a comprehensive and scalable solution that bridges the gap between AI and data governance.]]>
