If a car is subjected to freezing temperatures for extended periods, it might experience starting failures due to the icing of the starter solenoid contacts. These types of failures manifest unpredictably and without prior warning, making it challenging for car owners to anticipate them, particularly as there is no clear way to detect internal condensation. This study introduces a model that predicts the internal temperature using external data, aiming to estimate the condensation within the starter solenoid. Through experimental analysis of the failure mechanisms, key parameters leading to condensation were identified. Based on this analysis, critical environmental factors were identified, and representative datasets were collected using relative humidity and temperature (RH&T) sensors. Using this data, a model was developed to estimate the internal temperature based on the external temperature, and linear regression was applied. The performance of the predictive model was tested in a chamber that simulates varying temperature and humidity conditions. Experimental results indicated a temperature prediction error of 2 °C; the onset of condensation was detected within 1 min, and its duration was estimated at approximately 4 min. Additionally, the proposed model demonstrated its ability to classify icing failures with a 90% accuracy rate. One of the significant strengths of the research is its versatility and scalability, suggesting potential for broad applications in predicting condensation events.