Abstract: Disclosed are a method and an apparatus for real-time data monitoring based on machine learning, the method including: training a multi-layer predictor on actual values of historical indicator data, each layer of the multi-layer predictor including a plurality of predictors of different types; outputting predicted values of future indicator data by inputting a future time period for prediction into the trained multi-layer predictor; calculating alarm thresholds from the predicted values of the future indicator data and historical prediction errors; and triggering an alarm when an actual value of the future indicator data exceeds the corresponding alarm threshold. The accuracy of the alarm thresholds can be improved, and the alarm thresholds can be well adapted to the constantly changing indicator data. There is no need to manually configure a fixed alarm threshold, the accuracy of the alarm can be ensured, and the number of missed and false alarms can be reduced.