Abstract: A system and method for anomaly prediction in a plurality of applications to maintain operability of the system. Real-time data from the plurality of applications can be received, and for each desired variable in the data an entropy that can indicate an anomaly in the respective data. The data can be divided into batches of fixes size and a plurality of machine learning (ML) models applied to each of the batches to determine whether the data contains an anomaly or is about to contain an anomaly. The plurality of ML models can be trained based on historical data from the plurality of applications. A large language model (LLM) can be applied to the output of the plurality of ML models to evaluate performance/fine tune the plurality of ML models, and an alert can be transmitted to a system that automatically executes script(s) to take action to avoid the anomaly.