The use of large language models (LLMs) as dynamic repositories of knowledge is becoming increasingly prevalent. However, these models face significant challenges in managing outdated, erroneous, or privacy-sensitive information. The capacity to edit knowledge in an expedient manner within these models, without recourse to costly retraining, has emerged as a pivotal area of investigation. The existing techniques for editing knowledge are, on the whole, effective; however, they frequently lack robustness, particularly when applied across multiple languages. This thesis explores the domain of multilingual knowledge editing using Multi Lingual models like Llama-2, with a particular focus on enhancing the models' ability to update their knowledge efficiently and accurately in a multilingual context.