Abstract: As the core component of continuous casting machines, complex behaviors of fluid flow, heat transfer, mass transfer, and solidification occurring inside the mold are the key factors affecting the slabs quality. Breakout is one of the most catastrophic accidents in continuous casting process, which brings severe impacts on personal security, smooth producing, slab quality, and caster equipment. In particular, with the development of the high-speed casting technology, quality defects and sticking breakouts caused by high-load emerge frequently and missing or false alarms for online prediction of breakout occasionally occur. Thus, accurate identification and prediction for the mold breakout is a top priority for online processing control. Considering the typical temperature characteristics of “time lag” and “space inversion” during a breakout, this paper introduced the concepts of dynamic time warping (DTW) and density-based spatial clustering of applications with noise (DBSCAN) in machine learning. On the basis of collecting and distinguishing the typical change modes of mold temperature, an integrated novel method for predicting breakout was developed. The proposed method applied DTW to measure the similarity of mold thermocouple temperature under different casting speeds, steel grades, and other operating conditions, while DBSCAN was used to cluster and separate the temperature samples between normal casting status and sticking breakout. On the basis of the above mentioned method, the results show that the mold sticking breakout can be effectively detected and predicted. Compared with the traditional method based on logical judgment and artificial neural network, the clustering-based breakout prediction method does not require manual setting of thresholds or parameters. According to the common rule of temperature variation in historical samples of breakout, the typical characteristics of temperature in time and space can be extracted and fused, and the breakout can be accurately distinguished and predicted, which shows good self-adaptability and robustness.