The amount of data we produce each day in every sector keeps booming. This is attributable to the accelerating growth of the Internet of Things (IoT) technologies. Data fusion has a lot of applications in the area of complex industrial process control. The concept of data fusion can also be used to measure and detect a fault in ultrahigh-precision manufacturing assets. Predictive maintenance owes much of its promise and success to effective machine learning, artificial intelligence, big data analytics, etc. that are widely applied to offer digital twin simulation, analysis, and prediction. The aim of this research is to study how data from multiple sensors could be analytically explored and integrated for the purpose of predictive maintenance. In other to understand the concept behind this article, public data from Microsoft Azure is used to build a predictive maintenance model using supervised learning algorithms. These algorithms can be used for predictive maintenance of machines and equipment in many manufacturing industries.