The problem I intend to address in my research is the lack of ability of conventional Intrusion Detection Systems to properly handle the complexities and changing nature of Industrial Internet of Things environments. Existing IDS systems, which are primarily based on deep learning approaches, usually face high computing costs, limited real-time adaptability, and a lack of transparency in decision-making processes. Is it possible to create an adaptable and interpretable system that uses several data sources and hybrid decision models to detect intrusion in IIoT networks better? In this study, I describe a new multilayered intelligent decision support system that uses data fusion, fuzzy logic, and hybrid decision models. This approach is meant to improve detection accuracy while eliminating false situations.