This research work presents a novel approach to enhancing masked word prediction and sentence-level semantic analysis in Tamil language models. By synergistically combining BERT and Sentence-BERT (SBERT) models, we leverage the strengths of both architectures to capture the contextual understanding and semantic relationships in Tamil Language sentences. Our methodology incorporates sentence tokenization as a crucial pre-processing step, preserving the grammatical structure and word-level dependencies of Tamil sentences. We trained BERT and SBERT on a diverse corpus of Tamil data, including synthetic datasets, the Oscar Corpus, AI4Bharat Parallel Corpus, and data extracted from Tamil Wikipedia and news websites. The combined model effectively predicts masked words while maintaining semantic coherence in generated sentences. While traditional accuracy metrics may not fully capture the model’s performance, intrinsic and extrinsic evaluations reveal the model’s ability to generate contextually relevant and linguistically sound outputs. Our research highlights the importance of sentence tokenization and the synergistic combination of BERT and SBERT for improving masked word prediction in Tamil sentences.