Hateful memes are difficult to detect because their harmful meaning often emerges from the interaction between image and text rather than from either modality alone. This challenge becomes more severe in multilingual settings, where memes frequently contain code-mixed language and culturally grounded references. Existing Indic hateful meme datasets lack scale, language coverage, or task diversity, and current multimodal methods often overlook cross-modal semantic divergence. We introduce IndicHM, a trilingual hateful meme benchmark containing over 15,004 manually annotated image-text pairs in English, Hindi, and Bengali, including code-mixed content, supporting both hate detection and target classification. We also propose IndicHMNet, a disagreement-centric framework that decomposes representations into shared agreement and modality-specific residual subspaces and propagates the resulting disagreement-aware signal through multimodal fusion and contrastive representation learning. The proposed framework adaptively emphasizes modality-specific semantic divergence during fusion and optimization, enabling improved modeling of complex hateful meme semantics. Experiments across all languages and tasks demonstrate consistent gains over strong multimodal and vision-language baselines.