Recent advances in neural machine translation have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of neural machine translation. In this context, we present a review of the neural machine translation technology and the results from a large-scale case study of the practical application of neural machine translation in the Turkic language family in order to realize the applicability of prominent architectures and learning methods, data sets as well as evaluation metrics in languages with different characteristics and under high-resource to extremely low-resource scenarios, in addition to identified limitations and promising directions for research to contribute to the extension of the applicability of translation technology in more languages and domains.