The oil level is an essential indicator of the internal temperature and overall health of the transformer. The real-time monitoring of transformer oil levels allows for the avoidance of equipment damage and economic loss, as well as an improvement in the reliability and stability of the power system. This chapter proposes an intelligent error compensation method for transformer oil level monitoring based on the SSA-XGBOOST stacking model. This method reduces the amount of computation, noise, and redundancy by principal component analysis. The SSA algorithm is optimized using adaptive hyperparameters and hybrid variational strategies. Furthermore, the sparrow search algorithm (SSA) is employed to optimize the learning rate, tree depth, and maximum number of iterations of XGBoost, thereby balancing the performance of XGBoost. Consequently, the speed of model exploration and training is accelerated, ensuring the timeliness and accuracy of transformer oil level prediction.