Predictive maintenance is a crucial component in reducing the downtime and ensuring operational efficiency in hydraulic systems. The advent of ML techniques is a driving factor in the evolution of condition monitoring from basic diagnostic tools to sophisticated systems aiding in early detection of faults and proactive maintenance. Recent research has demonstrated the efficacy of ML models, including deep learning, ensemble learning and hybrid approaches in improving fault diagnosis for hydraulic systems. This study evaluates the performance of three state-of-the-art ensemble learning models—Random Forest (RF), XGBoost, and CatBoost—using IoT sensor data to classify defects in hydraulic coolers, a component of hydraulic systems. The study conducts a comprehensive comparative analysis and discusses the performance of the three models. From the findings, it can be inferred that all the three models generalize well, with the CatBoost model demonstrating superior performance in terms of accuracy and robustness. These results indicate that ensemble learning techniques, especially CatBoost, has proven to be a potential candidate in enhancing fault detection and predictive maintenance in industrial applications. The state of condition monitoring in hydraulic systems is improved by this research, which also offers valuable insights for the creation of scalable, real-time solutions that will save maintenance costs and increase operational reliability in hydraulic system-dependent sectors.