Magnetic components play a key role in energy transfer, storage, and filtering, directly affecting the size, weight, loss, and cost of power converters. Therefore, accurate prediction of core loss is essential. To address the significant influence of excitation waveforms on core loss, an ensemble learning-based waveform classification strategy is proposed. Support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) are used as base classifiers. The classification outputs are combined with original features to construct a new feature set, which is then used to train a meta-classifier to enhance generalization. XGBoost is selected as the core model for core loss prediction. A genetic algorithm is applied for multi-objective optimization to identify the optimal operating condition with minimal core loss and maximal magnetic energy transfer. Experimental results show that the ensemble classification model can accurately classify excitation waveforms. Compared with traditional core loss prediction models and other machine learning methods, the XGBoost model demonstrates higher prediction accuracy and better regression performance. The optimized framework demonstrates the capability to meet both loss reduction and energy efficiency objectives.