<p>This study, situated in the philosophy of science, introduces a novel conceptual and epistemological framework, the Dynamic Multi-Level Grey Space, to expand and deepen the foundational understanding of Grey Systems Theory (GST) by integrating the evolving structure of belief systems. The primary objective is to philosophically reconceptualize the notion of “grey space” as a dynamic and multi-layered epistemic medium that reflects cognitive processes, belief-based interpretations, and the evolving nature of human understanding in the face of incomplete, dynamic, and context-dependent information. In this context, fundamental concepts such as belief, certainty, and uncertainty are first reinterpreted through an epistemological perspective and then modeled using formal mathematical structures. The proposed framework introduces dynamic and multi-level models for key elements of GST, including grey spaces, grey numbers, and degrees of greyness, all of which can evolve over time as new data becomes available and contextual conditions change. As a result, this study shifts GST from a static, flat, and non-interactive paradigm to a dynamic, multi-layered, and interpretive model. This shift allows for a more accurate representation and analysis of uncertainty in real-world conditions, where human cognition and belief systems are inherently structured across multiple levels and continuously evolving. The core innovation of this research lies in offering a cognition-centered model of uncertainty that builds a conceptual bridge between philosophy, epistemology, and formal mathematics. This framework offers a promising foundation for applications in the development of intelligent decision-support systems, the analysis of complex human-centered environments, and the design of interpretable artificial intelligence architectures.</p>