This paper provides a comprehensive survey of explainable predictive maintenance (XPM), focusing on the integration of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (iML) techniques to enhance the trust and reliability of predictive maintenance systems. The authors categorize XPM methods into model-agnostic, model-specific, and combination approaches, detailing various methods such as SHAP, LIME, CAM, GradCAM, DIFFI, LionForests, saliency maps, and ARCANA. The paper also discusses the challenges and future research directions in XPM, emphasizing the importance of human-centered processes in Industry 5.0. The literature review follows the PRISMA 2020 guidelines, covering a wide range of studies from different databases, and aims to provide a systematic overview of the current state of XPM.This paper provides a comprehensive survey of explainable predictive maintenance (XPM), focusing on the integration of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (iML) techniques to enhance the trust and reliability of predictive maintenance systems. The authors categorize XPM methods into model-agnostic, model-specific, and combination approaches, detailing various methods such as SHAP, LIME, CAM, GradCAM, DIFFI, LionForests, saliency maps, and ARCANA. The paper also discusses the challenges and future research directions in XPM, emphasizing the importance of human-centered processes in Industry 5.0. The literature review follows the PRISMA 2020 guidelines, covering a wide range of studies from different databases, and aims to provide a systematic overview of the current state of XPM.