This study explores paraphrase identification for the Tamil language across various domains, including medicine, agriculture, and education, using SBert-based models. A unique dataset was compiled from Tamil standard newspapers, with sentence pairs manually labeled as paraphrase (1) or non-paraphrase (0) to ensure precise and domain-specific classification. Fine-tuning the SBert models on this dataset demonstrated enhanced performance, achieving accuracy levels between 70% and 80%. This research highlights the efficiency of transformer-based models and human-annotated datasets in paraphrase identification for Tamil, a highly agglutinative language. The findings contribute to advancing the understanding of natural languages, particularly complex forms such as Tamil.