Predictive maintenance (PdM) is a strategy that utilizes data and analytics to anticipate the failure of a component in a real system and detect any abnormalities, allowing for maintenance to be conducted before a breakdown occurs. Predictive maintenance (PdM) is a strategy that seeks to anticipate equipment malfunctions in advance, with the goal of reducing downtime and minimizing maintenance expenses. Utilizing advanced technologies like as data analytics and artificial intelligence (AI) improves the efficiency and precision of predictive maintenance systems, while also enhancing their ability to operate independently and adapt to complex and ever-changing work settings. This paper examines predictive maintenance methodologies for accurately forecasting the precise occurrence of faults. We will discuss techniques such as machine learning, statistical methodologies, and time-series analysis. This paper provides an overview of the latest advancements in AI-based Predictive Maintenance (PdM), with a specific emphasis on important elements, reliability, and upcoming trends. In addition, the paper incorporates examples from several fields, discusses known datasets used for predictive maintenance. After introducing techniques for computing and forecasting the occurrence of malfunctions, we conclude by providing suggestions for adopting accurate Predictive Maintenance (PdM) strategies.