The exponential growth of digital data and the acceleration of technological innovation require organizations to adopt intelligent and adaptive data architectures. However, fragmented systems and manual processes hinder optimal and responsive data utilization. This study aims to design an Artificial Intelligence (AI)-based data architecture framework that enhances automation, speed, and accuracy in data management, migration, and governance. The research approach follows three initial stages: (1) problem identification and motivation, (2) defining the objectives of a solution, and (3) design and development of the framework. The proposed framework consists of eight core stages—Data Source, Ingestion, Integration and translation, Storage, Cataloging, Governance and security, Processing, and Analytics—each linked to a corresponding AI adoption function. These functions include AI-assisted input, AI filtering, AI-optimized storage, AI enrichment, AutoML pipeline, AI harmonization, AI-augmented insight, and AI compliance layer. Each function strategically adopts AI to automate data acquisition, streamline integration and migration, accelerate predictive analytics, enrich metadata, and ensure compliance with data governance standards. This AI-based framework is expected to serve as a holistic model for modernizing organizational data systems, strengthening security, and accelerating data-driven decision-making.