<p>The rapid expansion of digital media ecosystems has intensified global concerns regarding misinformation, disinformation, and information integrity. Considerable scholarly attention has been devoted to the detection and mitigation of fake news through fact-checking organizations, artificial intelligence (AI) systems, and platform governance mechanisms. However, an underexplored phenomenon concerns instances in which information initially classified as fake news subsequently proves to be accurate, partially accurate, or contextually justified. This paper introduces the concept of the Non-Fakeness of Fake News (NFFN), referring to situations where misinformation labels themselves become erroneous due to evolving evidence, epistemic uncertainty, algorithmic limitations, or institutional biases. Building upon information integrity theory, epistemic uncertainty theory, trustworthy AI frameworks, and media governance perspectives, the paper develops a framework explaining how false misinformation classifications emerge and propagate. Eight theoretical propositions are proposed linking evidence maturity, transparency, uncertainty disclosure, algorithmic explainability, political polarization, media literacy, and public trust. The study further presents a governance framework designed to support adaptive and uncertainty-aware verification systems. The findings suggest that misinformation management should move beyond binary truth classifications and adopt dynamic verification mechanisms capable of revising conclusions as evidence evolves. The proposed framework contributes to information science, media studies, artificial intelligence governance, and communication research by introducing a theoretical lens through which the reliability of misinformation labeling itself can be critically evaluated. It further distinguishes defensible evidence-driven revision from avoidable verification failure, integrates retrieval-grounded LLM fact-checking, and specifies an empirical validation roadmap.</p>