Multimodal Multilabel Emotion Recognition (MMER) aims to identify multiple emotions from heterogeneous modalities. Two challenges of MMER are modality imbalance and insufficient modal interaction, which lead to limited representation capacity. In this paper, we propose a novel Quaternion-based Modality Balancing framework (QMB) to address these challenges through two key components: (1) Adversarial Temporal Masking (ATM) strategy is introduced to mask emotionally salient segments of dominant modality during training, thereby encouraging the model to attend to underutilized modalities and learn more balanced representations. (2) Hypercomplex Quaternion Fusion (HQF) module that projects modality-specific features into a quaternion space and performs Hamilton product operations, which enables efficient modeling of high-order inter-modal interactions while preserving modality specific semantics. We evaluate our method on two benchmark datasets, CMU-MOSEI and M3ED. Experimental results show that our method outperforms existing methods and achieves new state-of-the-art on both datasets.