This research introduces a novel second-order model temperature compensation (SMTC) method, optimized for high-accuracy temperature measurements in environments characterized by significant interference. Traditional methods, such as mean filtering and wavelet filtering, need long sampling time to obtain enough data for filtering, and long sampling time leads to obvious self-heating effect, which affects the measurement accuracy. To overcome these challenges, this research utilizes a second-order heat transfer model of resistance temperature detectors (RTDs), calibrated through self-heating experiments. This calibration process crucially defines the system’s transfer function, enabling the SMTC method to more accurately interpret temperature readings from noisy data. Through theoretical calculation, the maximum theoretical error of this method is 1.54%. At the same time, the experiments were carried out at $20~^{\circ }$ C– $160~^{\circ }$ C in air and $0~^{\circ }$ C– $80~^{\circ }$ C in water, and the results showed that the maximum mean measurement error was −1.36%, which was better than −2.03% of the mean filtering method and +2.31% of the wavelet denoising method. The SMTC method proposed in this article has better stability compared to mean filtering and better accuracy compared to the wavelet denoising method. The method’s effective error compensation, particularly for self-heating effects, signifies its potential for broad application in industries where accurate temperature control and monitoring are critical.