The stability of mold level fluctuations (MLFs) is crucial for product quality and process efficiency in continuous casting. Abnormal mold level fluctuations, which are typically associated with multiple factors including stopper rod opening, casting speed, and mold width, are known to lead to slab quality defects. In this paper, an Informer-based prediction framework is proposed for the early detection of abnormal MLF. A threshold-based labeling method is developed to quantify the future likelihood and severity of anomalies across different time horizons. Considering the importance of frequency-domain features in mold level prediction, power spectral density (PSD) features are incorporated and smoothed using the exponential moving average (EMA) to enhance predictive performance. Through the integration of temporal and processed spectral features, early indicators of abnormality can be captured, and proactive warnings can be issued. The proposed architecture is validated using approximately 32.5 million data points from a real-world continuous casting process. This approach provides a robust and data-driven solution for predicting and diagnosing abnormal MLF events in continuous casting. Experimental results show that the mean ROC-AUC and PR-AUC reach 0.821 and 0.418, respectively.