The electronics embedded in SLB equipment are vital for the success of its operations. They provide support such as control, navigation, and front-end data acquisition from sensors. Due to the extremely challenging operating conditions in downhole tools (high pressure, high temperature, severe shock, and vibration), electronics can be subjected to complex failure modes leading to operational downtime. Highly accurate models are key to better understanding these phenomena and to taking preventive measures. Physics-based models to predict time-to-failure and to investigate corner cases exist within SLB. However, to be able to capture the accuracy of the physics, those models are usually high-fidelity and, therefore, extremely time-consuming. They also do not capture uncertainties coming from, for example, manufacturing variabilities and they lack operational/testing correlation. On the other hand, data-driven models based on machine-learning methods are increasingly being applied with available data collected directly from sensors. These methods, although fast to be used, tend not to exhibit the capability of capturing complex relationships between the input parameters and desired output, which is difficult to describe without using physics. In this study, we explore hybrid reduced-order models of varying fidelity to understand the thermal behavior of a transistor prone to failure. We start with high-fidelity computational fluid dynamics (CFD) simulations to model the relationship between the operational parameters, like ambient temperature and flow velocity, with the temperature rise of the transistor. The data from the CFD simulations are then used to generate two reduced-order models—a lumped parameter thermal model to predict the maximum temperature of the transistor, and a machine-learning based model to predict the temperature field of the transistor. The models are further enhanced by using data from experimental tests to make them more accurate. Due to their significantly low computational effort, the reduced-order models developed in this study can be used in the field to monitor the temperature of the transistor in real time. This can enable us to identify when the transistor exceeds a critical temperature and how much time it spends above that temperature. This information can be used as a guide to determine if the transistor needs to be subjected to diagnostic tests to verify its health.