In the dynamic landscape of industrial operations, ensuring machines operate without interruption is crucial for maintaining optimal productivity levels. Estimating the Remaining Useful Life within Predictive Maintenance is vital for minimizing downtime, improving operational efficiency, and prevent-ing unexpected equipment failures. Survival analysis is a beneficial approach in this context due to its power of handling censored data (here referred to industrial assets that have not experienced a failure during the study period). However, the black-box nature of survival analysis models necessitates the use of explainable AI for greater transparency and interpretability. This study evaluates three Machine Learning-based Survival Analysis models and a traditional Survival Analysis model using real-world data from SCANIA AB, which includes over 90% censored data. Results indicate that Random Survival Forest outperforms the Cox Proportional Hazards model and the Gradient Boosting Survival Analysis and Survival Support vector machine. Additionally, we employ SHAP analysis to provide global and local explanations, highlighting the importance and interaction of features in our best-performing model. To overcome the limitation of applying SHAP on survival output, we utilize a surrogate model. Finally, SHAP identifies specific influential features, shedding light on their effects and interactions. This compre-hensive methodology tackles the inherent opacity of machine learning-based survival analysis models, providing valuable insights into their predictive mechanisms. The findings from our SHAP analysis underscore the pivotal role of these identified features and their interactions, thereby enriching our comprehension of the factors influencing Remaining Useful Life predictions.