Maintenance 4.0 refers to the integration of Industry 4.0 technologies with maintenance practices to make maintenance more intelligent, predictive, connected and data-driven. It uses technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), cloud computing, big data analytics, digital twins, sensors, mobile applications and Computerized Maintenance Management Systems (CMMS). These technologies help organizations move from reactive maintenance to preventive and predictive maintenance, thereby improving maintenance management, decision-making and equipment availability. IoT-enabled sensors continuously monitor equipment parameters such as temperature, vibration, pressure, speed, lubrication condition and energy consumption. The collected data is transmitted to centralized maintenance platforms in real time. For example, vibration sensors installed on an industrial motor can identify abnormal vibration patterns indicating bearing wear. Maintenance personnel can receive an alert and inspect or replace the bearing before complete motor failure occurs. This reduces unexpected breakdowns and production downtime. Predictive maintenance is one of the major contributions of Maintenance 4.0. AI and machine learning algorithms analyse historical and real-time equipment data to identify failure patterns and predict the remaining useful life of components. Instead of replacing parts only according to fixed schedules, maintenance can be performed according to their actual condition. For example, an airline can use engine-monitoring