Misogyny has become a pervasive issue in digital spaces. Misleading gender stereotypes are getting communicated through digital content.This content is majorly displayed as a text-and-image memes. With the growing prevalence of online content, it is essential to develop automated systems capable of detecting such harmful content to ensure safer online environments. This study focuses on the detection of misogynistic memes in two Dravidian languages, Tamil and Malayalam. The proposed model utilizes a pre-trained XLM-RoBERTa (XLM-R) model for text analysis and a Vision Transformer (ViT) for image feature extraction. A custom neural network classifier was trained on integrating the outputs of both modalities to form a unified representation. This model predicts whether the meme represents misogyny or not. This follows an early-fusion strategy since features of both modalities are combined before feeding into the classification model. This approach achieved promising results using a macro F1-score of 0.84066 on the Malayalam test dataset and 0.68830 on the Tamil test dataset. In addition, it is worth noting that this approach secured Rank 7 and 11 in Malayalam and Tamil classification respectively in the shared task of Misogyny Meme Detection (MMD). The findings demonstrate that the multimodal approach significantly enhances the accuracy of detecting misogynistic content compared to text-only or image-only models.