The age of Industry 4.0 sees predictive maintenance highlighted as one of the key methods to enhance industrial system performance and reliability. As a result, traditional predictive techniques are very challenged by the unpredictable nature and fuzzy input via sensors and machinery. This chapter suggests a novel Fuzzy Logic-Based Predictive Maintenance methodology to address such issues. By using fuzzy logic controllers (FLCs), the system handles ambiguous signals from sensors and provides exact forecasts of probable failures of equipment. Direct data from the IoT devices is funneled to the fuzzy inference system to judge the conditions of the equipment, identify anomalous actions, and determine the expected remaining useful life (RUL) of critical parts. The use of fuzzy rules and membership functions within the model enables it to adapt smoothly to various operating scenarios, indirectly increasing the predictability, reliability, and scope of operation options. Successfully emulating the approach in manufacturing and practical use has shown its usefulness. The outcomes demonstrate more effective fault detection, better maintenance schedules, and, compared to traditional methods, reduced unplanned downtime, cost of maintenance, and increased resource efficiency. These findings make progress in the science of intelligent maintenance systems and promote the transition to effective, sustainable manufacturing processes.