Availability of the user reviews in vernacular languages is helpful for the users to get information regarding the products. Since most of the e-commerce websites allow the reviews in English language only, it is important to provide the translated versions of the reviews to the non-English speaking users. Translation of the user reviews from English to vernacular languages is a challenging task, predominantly due to the lack of sufficient in-domain datasets. In this paper, we present a pre-training based efficient technique which is used to adapt and improve the single multilingual neural machine translation (NMT) model for the low-resource language pairs. The pre-trained model contains a special synthetic cross-lingual decoder. The decoder for the pre-training is trained over the cross-lingual target samples where the phrases are replaced with their translated counterparts. After pre-training, the model is adapted to multiple samples of the low-resource language pairs using incremental learning that does not require full training from the very scratch. We perform the experiments over eight low-resource and three high resource language pairs from the generic domain, and two language pairs from the product review domains. Through our synthetic multilingual decoder based pre-training, we achieve improvements of upto 4.35 BLEU points compared to the baseline and 2.13 BLEU points compared to the previous code-switched pre-trained models. The review domain outputs from the proposed model are evaluated in real time by human evaluators in the e-commerce company Flipkart.