This paper presents a study on syllable segmentation in Tenyidie, a low-resource Tibeto-Burman language spoken by the Tenyimia community in the Nagaland region of northeastern India. Tenyidie is a tonal language with a subject-object-verb word order and a highly agglutinative nature. We constructed a corpus of 10,120 syllable-segmented Tenyidie words and applied LSTM, BLSTM, BLSTM+CRF, and encoder-decoder deep learning architectures. Using a dataset split ratio of 80:10:10 (training:validation:test), the BLSTM model achieved a peak accuracy of 99.21%.