This paper presents the Mongolian Meta-Adapter (MMA), An innovative methodological system developed to simultaneously mitigate lexical formation intricacies and annotation scarcity in Mongolian affective computing. MMA integrates three key components: a rule-based morphological tokenizer, a parameter-efficient meta-learning scheme, and cross-lingual adversarial training. To preserve the semantic consistency of Mongolian’s agglutinative structure, we develop a rule-guided morphological parser with auto-annotation, achieving 92.7% segmentation accuracy. A morphology-attentive gating module further emphasizes root-level sentiment signals and leads to a 5.2% improvement in Macro-F1. For efficient adaptation, we combine low-rank adapters (LoRA, rank 8) with dynamically generated meta-tasks, reducing trainable parameters to less than 10% of the full model and improving generalization under limited supervision. In addition, adversarial training with a gradient reversal layer enhances cross-domain robustness, reducing the performance drop from news to social media by 27%. Evaluated on a 7,000-sample, 8-category Mongolian sentiment dataset we constructed, MMA achieves 83.5% Macro-F1, outperforming both XLM-RoBERTa full fine-tuning (74.4%) and conventional meta-learning baselines (78.3%). Our work introduces the first morphology-aware sentiment analysis system for Mongolian and provides a transferable approach for other low-resource agglutinative languages.