Machine Translation (MT) is a rapidly expanding field, experiencing significant advancements in recent years with the development of models capable of translating multiple languages with remarkable accuracy. However, the representation of African languages in this field still raises problems due to linguistic complexities and limited resources. This applies to the Zarma language, spoken by over 5 million people across Niger and neighboring countries. In this paper, we introduce Feriji, the first robust French-Zarma parallel corpus and glossary designed for MT. This dataset is a major step in addressing the lack of resources for Zarma, a dialect of the Songhai, part of the Nilo-Saharan language family. We focused on not only bridging a significant language gap but also promoting an indigenous African language.