With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world. Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automatically compares the fed data and the information from previous cases to anticipate or predict equipment failure before it happens, thus helping optimise equipment utilisation and maintenance strategies, improve performance and productivity, and extend equipment life. Robust PdM tools enable organisations to leverage and maximise the value of their existing data to stay ahead of potential breakdowns or disruptions in services, and address them proactively instead of reacting to issues as they arise. Therefore, it has attracted more and more attention of specialists in recent years. This paper provides a comprehensive review of the recent advancements of machine learning (ML) techniques widely applied to PdM by classifying the research according to the ML algorithms, machinery and equipment used in data acquisition. Important contributions of the researchers are highlighted, leading to some guidelines and foundation for further studies. Currently, BIENDONG POC is running some pilot PdM projects for critical equipment in Hai Thach - Moc Tinh gas processing plant.