This research explores word alignment in low-resource languages, specifically focusing on Telugu and Tamil, two languages within the Dravidian language family. Traditional statistical models such as FastAlign, GIZA++, and Eflomal serve as baselines but are often limited in low-resource settings. Neural methods, including SimAlign and AWESOME-align, which leverage multilingual BERT, show promising results by achieving alignment without extensive parallel data. Applying these neural models to Telugu-Tamil and Tamil-Telugu alignments, we found that fine-tuning with POS-tagged data significantly improves alignment accuracy compared to untagged data, achieving an improvement of 6–7%. However, our combined embeddings approach, which merges word embeddings with POS tags, did not yield additional gains. Expanding the study, we included Tamil, Telugu, and English alignments to explore linguistic mappings between Dravidian and an Indo-European languages. Results demonstrate the comparative performance across models and language pairs, emphasizing both the benefits of POS-tag fine-tuning and the complexities of cross-linguistic alignment.