This research explores the integration of fuzzy logic algorithms with Internet of Things (IoT) enabled cyber-physical systems (CPS) for enhanced decision-making capabilities in smart industry and agriculture applications. The study addresses the challenges of uncertain and imprecise data processing in complex industrial and agricultural environments by implementing adaptive fuzzy inference systems that optimize resource allocation, improve operational efficiency, and enable real-time monitoring and control. Experimental results demonstrate significant improvements in production efficiency, resource utilization, and sustainability metrics across diverse use cases including precision agriculture, industrial automation, and supply chain management. The proposed framework successfully bridges the gap between theoretical fuzzy logic principles and practical IoT implementation, providing a robust methodology for dealing with ambiguity and variability in sensor data while facilitating more intelligent automated decision-making processes. This work contributes to the advancement of Industry 4.0 and Agriculture 4.0 paradigms by establishing a flexible and scalable approach to intelligent system design that can adapt to dynamic environmental conditions and evolving operational requirements.