The advent of industry 4.0 (I4.0) has brought about significant advancements in manufacturing processes, leveraging advanced sensing and data analytics technologies to optimize efficiency. Within this paradigm, predictive maintenance (PdM) plays a crucial role in ensuring the reliability and availability of production systems. There are several existing approaches for PdM in I4.0, each with its own advantages and disadvantages. In this paper, we review the state-of-the-art related to PdM approaches in the context of I4.0. Our systematic literature review encompasses a comprehensive analysis of recent research, focusing on the different AI-based techniques employed in PdM applications. Through this survey, we aim to provide valuable insights into the current landscape of PdM methodologies and foster future innovations in this rapidly evolving field.