Large Language Models (LLMs) have demonstrated significant capabilities across numerous application domains. A key challenge is to keep these models updated with latest available information, which limits the true potential of these models for the end-applications. Although, there have been numerous attempts for LLMs’ Knowledge Editing (KE), i.e., to update and/or edit the LLMs’ prior knowledge and in turn test it via Multi-hop Question Answering (MQA), yet these studies are primarily focused on English language. In this paper, we extend MQA-Ke for Arabic language. For this, we propose: Multi-hop Questioning Answering under Knowledge editing for arabic language (MQA-Keal). MQA-Keal stores knowledge edits as structured knowledge units in the external memory. In order to solve multi-hop question, it first uses task-decomposition to decompose the question into smaller sub-problems. Later, for each sub-problem it iteratively queries the external memory and/or target LLM in order to generate the final response. In addition, we also contribute MQuAKE-ar (Arabic translation of English benchmark MQuAKE), as well as a new benchmark MQA-Aeval for rigorous performance evaluation of MQA under Ke for Arabic language. Experimentation evaluation reveals MQA-Keal outperforms the baseline models by a significant margin. We release the codes for MQA-Keal at https://github.com/asif6827/MQA-Keal.