Since the last decade, aircraft systems, such as flight control and landing gear, have been requiring increasing power, and consequently, the complexity of hydraulic aircraft systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of hydraulic aircraft systems that are dispersed around an aircraft and supply power to critical flight systems. This study proposes a novel digital twin-based health monitoring system for aircraft hydraulic systems to enable diagnostics of system failures early in the design cycle using machine learning (ML) methods. The scope of the systems is limited to hydraulic systems at the aircraft level using 20 failure scenarios. The support vector machine and several ensemble learning algorithms of ML methods were used to identify these failures. A comparison of the ML methods revealed that the random forest algorithm performed superior to the other ML algorithms. The developed digital twin framework for hydraulic system of aerial vehicle platforms, can help researchers and engineers to evaluate diagnostics systems early in the design phase.