Measuring core body temperature has significant implications in various fields, including healthcare, sports medicine, and military operations. Existing core body temperature measurement, such as rectal and esophageal measurements, are invasive, costly, and may not provide continuous monitoring, highlighting the need for non-invasive and reliable alternatives. With the development of wearable and computer technology, predicting core body temperature based on continuous measurement of skin temperature, ambient temperature, and other physiological parameters has become a trend for future development. In this study, we proposed a novel non-invasive method for predicting core body temperature using wearable sensors. A stacking fusion model was proposed to improve the core temperature prediction accuracy. The skin temperature and ambient temperature were collected by a wearable device and processed to remove outliers. Simultaneously, the core body temperature collected by a capsule temperature sensor was used as the gold standard. Then, we used the preprocessed data to train a machine learning algorithm, which was validated on collecting human data. The results showed that the algorithm based on stacking model fusion performed best, with an RMSE of 0.0448, MAE of 0.0214, and MAPE of 0.0582%, indicating high precision in predicting core body temperature, which can be used for continuous monitoring and accurate diagnosis, effective medical treatment.