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Multi-modal retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is a huge manual effort. In this paper, we propose a framework MuMUR, that utilizes knowledge transfer from a multilingual model to boost the performance of multi-modal (image and video) retrieval. We first use state-of-the-art machine translation models to construct pseudo ground-truth multilingual visual-text pairs. We then use this data to learn a joint vision-text representation where English and non-English text queries are represented in a common embedding space based on pretrained multilingual models. We evaluate our proposed approach on a diverse set of retrieval datasets: five video retrieval datasets such as MSRVTT, MSVD, DiDeMo, Charades and MSRVTT multilingual, two image retrieval datasets such as Flickr30k and Multi30k . Experimental results demonstrate that our approach achieves state-of-the-art results on all video retrieval datasets outperforming previous models. Additionally, our framework MuMUR significantly beats other multilingual video retrieval dataset. We also observe that MuMUR exhibits strong performance on image retrieval. This demonstrates the universal ability of MuMUR to perform retrieval across all visual inputs (image and video) and text inputs (monolingual and multilingual).