Enterprise architecture change decisions frequently affect applications, data, integrations, business capabilities, infrastructure, security, compliance, and operating models across multiple domains. Traditional impact analysis is often manual, slow, and incomplete because dependency knowledge is distributed across repositories, documents, CMDBs, roadmaps, and subject-matter experts. This paper proposes an AI-Driven Enterprise Architecture Change Impact Analysis Framework that combines architecture knowledge graphs, metadata, dependency models, semantic search, machine learning, Generative AI, and policy rules to evaluate the likely effects of proposed change. The framework defines impact domains, confidence scoring, risk propagation, scenario comparison, evidence traceability, human validation, and governance controls. It supports application modernization, cloud migration, data-platform transformation, API changes, vendor replacement, and AI adoption. The proposed approach enables architects to identify hidden dependencies earlier, reduce design rework, improve decision quality, and create more defensible transformation roadmaps.

Sanjeeve Kumar Gajadi, "AI-Driven Enterprise Architecture Change Impact Analysis Framework" International Journal of Scientific Research and Engineering Development, V9(5): Page(1865-1867) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.