Multimodal content moderation in online social spaces is a critical safety challenge, yet existing systems are heavily dominated by monolithic cultural biases and perform poorly on underrepresented languages. In this paper, we study culturally grounded misogynistic meme detection under a novel multi-perspective evaluation framework, which simultaneously assesses models across Indian, Chinese, and Western cultural lenses. We propose MoEzhi-Guru (drawing on the Dravidian term mozhi for language and the Indic term guru for expert/guide), an adaptable, resource-efficient Multimodal Mixture of Experts (MoE) model designed to handle complex cross-cultural and cross-lingual shifts. The system encodes input using mDeBERTa-v3 and SigLIP to extract rich multilingual text and global visual semantics, then fuses and routes these features through a custom MoE layer utilizing dense soft routing. This gating mechanism dynamically weights specialized expert subnetworks, capturing localized socio-cultural nuances before predicting labels through culture-specific classification heads. We evaluate our framework in four typologically diverse language subsets: Tamil and Malayalam (underrepresented Dravidian scripts), Chinese (containing colloquial and code-switched text), and English. Our system demonstrates robust cross-lingual generalization, yielding an overall average score of 0.8515 Macro-F1 and consistently exceeding a Gemma3-4b baseline by more than 0.42 Macro-F1 points. This work provides a scalable, inclusive foundation for culturally aware safety systems in low-resource and multilingual environments.