With the increasing demand for higher bandwidth and frequency in high-speed digital systems, the interference of common-mode (CM) noise in differential signal transmission has become more severe. Common-mode suppression filters (CMF) were proposed to solve this issue, but their design process typically relies on empirical parameter tuning with extensive electromagnetic simulations, which not only increases design costs but also limits efficiency and flexibility. In this paper, a transformer-based inverse design method for CMFs is proposed for the first time, and it can eliminate the need for empirical parameter adjustments by automatically predicting the targeted geometric parameters, thereby improving the design efficiency. In addition, to address the problem of imbalanced data distribution, the multilabel synthetic minority over-sampling technique (MLSMOTE), which can enhance the data representation in sparse sample regions, is implemented. Further validation on tunable CMFs confirms that the proposed inverse design method has broad applicability. The experimental results demonstrate that the proposed inverse design method can accurately predict the geometric parameters and improve the efficiency, thereby providing an innovative solution for the design and applications of CMFs.