Machine translation is an application of NLP, which can be used to translate text from one natural language to another natural language. This research attempted to design Amharic-Kistanigna bidirectional machine translation. Previously, there is no research conducted on machine translation between Kistanigna and Amharic. Due to an increase in the number of language users, to addressing the issues of the endangered of the Kistanigna language and to increases the content of the language in web, it is essential to develop machine translation between Kistanigna and Amharic languages. Different official documents, news and other written texts in both languages needed to be translated in order to share information. Neural Machine Translation (NMT) is a recently proposed approach to machine translation (MT) that has achieved the state-of-the-art translation quality in recent years. Unlike traditional MT approaches, NMT aims to create a single neural network that can be tuned collaboratively to maximize translation performance. So, the aim of this study is to develop Amharic-Kistanigna bi-directional machine translation using Deep learning. We conduct the experiments using five encoder decoder models. These are LSTM, Bi-LSTM, LSTM+attention, CNN+attention, and Transformers. To conduct our experiments, we have used 9,225 parallel sentences using both word and morpheme translation unit. In order to segment our morpheme data, we have used morfessor tool. To propose an optimal model with best translation unit we consider efficiency (training time, memory usage, and BLEU score). Finally, we have proposed morpheme-based bi-directional machine translation using Transformers model with a BLEU score of 21.31 and 22.40 from Amharic-Kistanigna and Kistanigna-Amharic translation respectively. The major weakness of the study is unavailablity of enough dataset to conduct an extensive experiment. As a result, there is a need to prepare parallel corpora for conducting similar research. Keywords: Amharic-Kistanigna Machine Translation, Bi-Directioanl Machine Translation, Neural Machine Translation, Deep Learning Approach, LSTM, Bi-LSTM, LSTM+attention, CNN+attention, Transformers.