With the rapid expansion in the use of medical devices within hospitals and healthcare centers, the reliability of these devices has become a critical factor in ensuring the continuity and quality of healthcare services. Any sudden failure of a medical device may disrupt medical procedures or pose a direct risk to patients’ lives, which has driven healthcare institutions to seek intelligent solutions that minimize the likelihood of unexpected failures. In this context, intelligent models for predicting medical device failures before they occur have emerged through the integration of artificial intelligence and big data analytics. These models analyze operational data from medical devices, including historical performance records, usage patterns, and internal sensor data such as temperature, vibration, and energy consumption, in order to detect abnormal changes that may indicate an impending malfunction. These intelligent models rely on advanced machine learning and deep learning algorithms that are trained on large volumes of historical data to learn failure-related patterns. Over time, the models become capable of accurately predicting the likelihood and timing of device failures, allowing preventive actions to be taken before problems escalate. The operational process begins with continuous data collection from medical devices, followed by data preprocessing and cleansing to ensure analytical reliability. The models are then trained to extract hidden relationships and patterns within the data, ultimately generating early warnings that support timely maintenance decisions. This proactive approach helps reduce unexpected breakdowns, lowers emergency maintenance costs, and extends the operational lifespan of medical equipment. The implementation of these predictive models has a direct impact on patient safety by ensuring uninterrupted healthcare delivery and improving overall resource management within healthcare institutions. On a broader scale, adopting such intelligent technologies contributes to achieving the Sustainable Development Goals, particularly Goal 3 related to good health and well-being, as well as Goal 9 concerning industry, innovation, and resilient infrastructure. Despite their significant advantages, intelligent predictive models face future challenges related to handling massive volumes of data, the high cost of implementation, and the need to protect sensitive medical data from cybersecurity threats. Nevertheless, the future of healthcare systems is moving toward wider integration of these models within smart healthcare environments, supporting the development of safer, more efficient, and more sustainable healthcare services. University of Al-Mustaqbal – The First University in Iraq