Neural Machine Translation (NMT) has superseded Statistical Machine Translation (SMT) owing to the advent of deep learning in natural language processing. However, enhancing the quality of low-resource NMT involves more nuanced strategies beyond data-dependent methods, especially for languages like Thai that lack substantial parallel corpus data for Thai-English translation. Addressing this problem, we introduce a Thai-English NMT methodology that amalgamates local and global syllabic features. Our approach employs a CNN-based module for extracting local syllable characteristics and capturing interdependencies among Thai syllable characters. Concurrently, a global syllable feature extraction mechanism is used to preserve sentence-level syllable traits. The integration of these features significantly augments Thai syllable context representation. In experiments conducted on the Thai-English corpus from the AL T dataset, our method improved the BLEU score by 2.11 points above the baseline, achieving a score of 12.99. This research holds potential implications for the translation of other syllable-based, low-resource languages, as well.