A system for detecting abnormality of a plant, the system comprising: a data collection unit for collecting actually measured plant data; a data learning unit for generating learning data by extracting only plant data collected when the plant has been determined to be in a normal state and deleting plant data collected when the plant has been determined to be in the abnormal state; a modeling unit for generating a plurality of prediction models using the learning data extracted in the data learning unit, wherein the plurality of prediction models includes a plurality of parametric (161, 163) and non-parametric models (165, 167); a learning model selection unit for generating a plurality of optimized prediction models by selecting and combining a plurality of prediction models generated in the modelling unit; and an abnormality alarm unit comprising: a prediction algorithm unit using a plurality of the optimized prediction models provided from the learning model selection unit for generating for each of the provided optimized prediction models prediction data including a prediction value (EstimatedValue_mlrm(k)) and an error value (Residual mlrm(k)) based on the learning data; an ensemble learning unit for outputting optimal prediction data by performing ensemble learning based on the plurality of prediction data generated in the prediction algorithm unit, wherein in the ensemble learning unit a plurality of learning algorithms are used, and an alarm logic for determining whether or not the plant is in an abnormal state by calculating a difference between the optimal prediction data and the plant data collected in the data collection unit, wherein the alarm logic is configured to generate an alarm, when it is determined that the difference exceeds a predetermined allowance value. a data collection unit for collecting actually measured plant data; a data learning unit for generating learning data by extracting only plant data collected when the plant has been determined to be in a normal state and deleting plant data collected when the plant has been determined to be in the abnormal state; a modeling unit for generating a plurality of prediction models using the learning data extracted in the data learning unit, wherein the plurality of prediction models includes a plurality of parametric (161, 163) and non-parametric models (165, 167); a learning model selection unit for generating a plurality of optimized prediction models by selecting and combining a plurality of prediction models generated in the modelling unit; and an abnormality alarm unit comprising: a prediction algorithm unit using a plurality of the optimized prediction models provided from the learning model selection unit for generating for each of the provided optimized prediction models prediction data including a prediction value (EstimatedValue_mlrm(k)) and an error value (Residual mlrm(k)) based on the learning data; an ensemble learning unit for outputting optimal prediction data by performing ensemble learning based on the plurality of prediction data generated in the prediction algorithm unit, wherein in the ensemble learning unit a plurality of learning algorithms are used, and an alarm logic for determining whether or not the plant is in an abnormal state by calculating a difference between the optimal prediction data and the plant data collected in the data collection unit, wherein the alarm logic is configured to generate an alarm, when it is determined that the difference exceeds a predetermined allowance value. a prediction algorithm unit using a plurality of the optimized prediction models provided from the learning model selection unit for generating for each of the provided optimized prediction models prediction data including a prediction value (EstimatedValue_mlrm(k)) and an error value (Residual mlrm(k)) based on the learning data; an ensemble learning unit for outputting optimal prediction data by performing ensemble learning based on the plurality of prediction data generated in the prediction algorithm unit, wherein in the ensemble learning unit a plurality of learning algorithms are used, and an alarm logic for determining whether or not the plant is in an abnormal state by calculating a difference between the optimal prediction data and the plant data collected in the data collection unit, wherein the alarm logic is configured to generate an alarm, when it is determined that the difference exceeds a predetermined allowance value.