To address the data management limitations of traditional ontology models in dynamic industrial settings, this study introduces the Fractal Fuzzy Ontology Modeling (FFOM) framework, a novel methodology for optimizing data management, integration, and decision making. FFOM’s value is rooted in two major contributions: firstly, the strategic use of fractal structures to achieve unparalleled scalability and modularity, which significantly reduces the effort required during data hierarchy updates by enabling self-similar, expandable data architectures. Secondly, FFOM features the synergistic use of fuzzy logic to meticulously manage ambiguity and uncertainty, including the representation of imprecise relationships and support for flexible, rule-based reasoning. The practical value of this integrated approach is demonstrated through a mold assembly case study, which validates FFOM’s effectiveness in structuring complex data hierarchies, managing uncertainty, and enabling automated reasoning. Implemented in the Web Ontology Language (OWL) for standardization and interoperability purposes, FFOM ultimately provides a clear pathway toward developing more intelligent, adaptive, and scalable data ecosystems in demanding manufacturing domains, where real-time data analysis is critical.