Modeling and analyzing the thermal energy behavior of industrial hydraulic systems with useful hydraulic power of 20 kW or higher is a challenging task due to the system’s inherent complexity and the multitude of interacting components. Conventional thermal modeling approaches rely heavily on numerous interdependent parameters and sensor measurements, including temperature, pressure, and flow rate for each port in hydraulic component. This reliance often leads to prolonged modeling times, which can exceed the actual operational time of the hydraulic system by a factor of 2 to 6.5, resulting in costly and time-intensive analysis. To address these limitations and meet industrial demands, this study introduces an enhanced thermal model based on a new temperature parameter, termed the average transient temperature (T_avg), aimed at significantly reducing the modeling time to be shorter than the experimental operation time. Furthermore, Physics informed neural network model is used to determine hydraulic oil specifications during the modeling implementation as one correlation model.