Аннотация. Predictive maintenance is a method that helps predict potential equipment failures and avoid unexpected breakdowns. Recently, artificial intelligence (AI) and machine learning (ML) technologies have increasingly been used for predictive maintenance. These technologies allow significant reductions in emergency repair costs and extend the lifespan of equipment. This research aims to investigate predictive maintenance methods and adapt them for use in industrial sectors. The study uses the following methods: Analysis of historical failure data to predict future breakdowns; Use of sensors to monitor the equipment status in real time; Development of hybrid models that combine various approaches to improve prediction accuracy. The results of the study demonstrate that incorporating AI and ML technologies in predictive maintenance can help reduce repair and maintenance costs and improve equipment performance. Data from sensors allows for timely fault detection, preventing long downtimes. During the research, failure prediction models were developed that showed prediction accuracy of up to 98.7% with low error rates. These models can be effective not only for large enterprises but also for other industries, enhancing the overall efficiency of the industrial sector.