The article is devoted to the helicopter turboshaft engines gas temperature in front of the compressor turbine intelligent sensor development, which combines a classical physical and mathematical model with modern algorithms for adaptive signal processing and artificial intelligence. The main attention is paid to the mathematical model’s construction describing the heat exchange dynamics under conditions of a rapidly changing temperature flow, as well as the extended Kalman filter with a neural network integration for nonlinear measurement correction. A computational experiment conducted in the MATLAB Simulink environment with data from the Mi-8MTV helicopter’s TV3–117 engine showed that after intelligent correction, the measured temperature value approaches the true value: the maximum temperature reached 1140 K. The correction signal’s dynamics and the neural network training error’s convergence analysis confirmed the adaptation algorithm’s high efficiency, which allows for prompt compensation for noise and systematic errors using LEAN concepts framework in the data processing pipeline. The developed system demonstrates the using intelligent sensors promise to improve safety and optimize the helicopter turboshaft engines operation under dynamic operating conditions.