Predictive maintenance has become one of today's proactive maintenance strategies that use monitoring and data analysis methods to predict when equipment needs maintenance before it fails. Predictive maintenance is becoming very important for most organizations in maintenance preparation. Meanwhile, this study aims to explore predictive maintenance research-related publishing patterns and trends the most commonly used author keywords in the Scopus and Web of Science (WoS) databases. The eminent software, ScientoPy and VOSviewer, are used to run and execute relevant publication data retrieved from Scopus and WoS. The results showed a positive trend in the growth of predictive maintenance literature in both databases since 2014. The top three research areas that dominate this topic are “engineering”, “computer science”, and “operations research & management science”. Based on a country analysis, China has become an active publisher, followed by the United States and France. Importantly, this study emphasised the scholarly practices prevalent in predictive maintenance research have impressively propagated. The trends will assist researchers in recognizing various fields in identifying the core areas, proactive institutions, and productive authors published in this knowledge for supplementary investigation. Besides, by examining the most popular keywords, the results of this study enable researchers to discover the possibility for future research that may be conducted, particularly concerning the annual growth rates, which have been trending in the last five years.