This paper presents a novel framework that integrates intelligent computing techniques with modular IoT ontologies to address core challenges in Industry 4.0 and prepare for the human-centric paradigm of Industry 5.0. Traditional IoT ontologies often struggle with adaptability, scalability, and semantic interoperability, limiting their effectiveness in rapidly evolving industrial ecosystems. To overcome these limitations, the proposed framework combines truth table-based data structuring, vector-matrix automata for real-time classification and clustering, and in-memory computing for energy-efficient data processing. Experimental validation in a simulated IoT environment shows a 30% reduction in ontology update times, a 40% decrease in energy consumption, and a 95% classification accuracy rate—surpassing the performance of conventional ontology systems. These results underscore the framework’s potential for real-time decision-making, dynamic ontology maintenance, and seamless integration of heterogeneous IoT devices. By supporting modular ontology updates and incorporating human feedback loops, this research establishes a foundation for Industry 5.0, where intelligent machines and human creativity collaborate for enhanced productivity and innovation. The proposed approach not only optimizes current IoT deployments but also offers a scalable blueprint for future, human-centric industrial ecosystems.