Grapheme-to-Phoneme (G2P) conversion is a necessary step for speech synthesis and speech recognition. In this paper, we attempt to apply a Statistical Machine Translation (SMT) approach for Myanmar G2P conversion. The performance of G2P conversion with SMT is measured in terms of BLEU score, syllable phoneme accuracy and processing time. The experimental results show that G2P conversion with SMT is outperformed a Conditional Random Field (CRF) approach. Moreover, the training time was considerably faster than the CRF approach.