The increasing adoption of Internet of Things (IoT) devices in healthcare has created new opportunities for optimising medical equipment maintenance and enhancing patient care. Predictive maintenance, enabled by machine learning techniques, has emerged as a promising strategy for improving the dependability and effectiveness of healthcare IoT systems. This research paper presents a thorough examination of the application of machine learning for predictive maintenance in the Internet of Things (IoT) for healthcare. Embedded Internet of Things (IoT) sensors in medical devices such as vital signs monitors, infusion pumps, and imaging systems are utilised to collect data for the proposed method. These sensors continuously collect data in real time regarding the performance, operating conditions, and environmental factors of the equipment. Advanced machine learning algorithms are applied to the collected data to identify patterns and trends indicative of equipment failures or degradation. The research investigates various machine learning techniques, such as supervised and unsupervised learning, to develop accurate predictive models for IoT maintenance in healthcare. The models are trained with historical data to determine the relationship between sensor readings and maintenance events. The models are then deployed to predict potential equipment failures and trigger proactive maintenance actions, thereby minimising downtime and ensuring uninterrupted patient care