Industry 4.0 has become increasingly integrated into the mechanical and aerospace industries, since the inception of the Internet of Things (IoT), sensors, and big data. Aircraft engines are intricate mechanical systems that require constant monitoring to ensure airworthiness, constituting approximately 35–40% of the total maintenance expenditure. The remaining useful life (RUL) of a jet engine is crucial for its operational efficiency, as it determines how long the engine can operate safely before Maintenance, Repair, and Operations (MRO) workshop visit. The process of determining the RUL is known as prognostics. Traditionally, data-driven prognostic methods rely on run-to-failure data, referred to as training data. The National Aeronautics and Space Administration (NASA) has provided the Commercial Modular Aero-Propulsion System Simulation (C–MAPSS) dataset, which is utilized for training our machine learning (ML) models. This investigation presents a probabilistic ML method, namely Gaussian Process Regression (GPR), which demonstrates superior effectiveness compared to a deterministic approach for training ML algorithms for data-driven prognostics. GPR is a Bayesian approach that does not require specific parameter values for regression problems. Three deterministic algorithms, namely Multi-layer Perceptron (MLP), Support Vector Regression (SVR), and Relevance Vector Regression (RVR), are compared with GPR. A scoring metric provided by NASA is used to compare the deterministic and probabilistic approaches. The MLP, SVR, RVR, and GPR scores are 18000, 1380, 1500, and 1045, respectively. The root mean square error (RMSE) for the different methods is 37.56, 20.96, 23.80, and 19.49, respectively. Among all the methods, the probabilistic GPR model achieves the best NASA score and the lowest RMSE, highlighting its superior performance over the deterministic methods.