Suicide is a critical global health issue with individuals increasingly making suicidal intentions via social media. Even though NLP models have extensively been used for the detection of suicide ideation, cross-lingual difference bars them from optimal performance in multilingual settings. To counter this, we introduce M3V-BERT (Multilingual Multimodal Vision BERT) in the text-only case, capitalizing on mBERT’s multilingual knowledge and cross-lingual embeddings to enhance semantic understanding across languages. In contrast to traditional models, M3V-BERT needs no language-specific resources, which makes it versatile for both high- and low-resource languages. This model incorporates ensemble learning, RoBERTa-CNN, and big data analytics to improve contextual understanding and classification accuracy. Experimental findings on multilingual social media posts demonstrate that M3V-BERT performs much better than baseline NLP models with substantial accuracy, precision, and recall improvements. Its ability to generalize across languages positions it well as a suitable choice for the detection of suicide ideation in multilingual scenarios, particularly for low-resource languages where traditional models struggle to perform well. Addressing cross-lingual challenges in suicide ideation detection, this work illustrates the potential for scalable, culture-sensitive multilingual NLP structures in predicting mental health risk. Our research pushes the state of the art in developing cross-lingual, transformer NLP models able to identify signs of distress within different linguistic environments. This shall be further be extended to multimodal environment in future work based on visual signs from social media updates to bolster suicide risk forecasting.