A machine learning (ML) method aided by domain knowledge was proposed to predict saturated magnetization (B_s) and critical diameter (D_max) of soft magnetic metallic glasses (MGs). Two datasets were established based on published experimental works about soft magnetic MGs. A general feature space was proposed and proven to be adaptive for ML model training for different prediction tasks. It was demonstrated that the predictive performance of ML models was better than that of traditional knowledge-based estimation methods. In addition, domain knowledge aided feature design can greatly reduce the number of features without significantly reducing the prediction accuracy. Finally, the binary classification of D_max of soft magnetic MGs was studied.