<p>As our world becomes more connected, the need for advanced translation technology to bridge language barriers and facilitate communication across diverse communities has grown significantly. However, current Speech Translation (ST) systems struggle with accurately translating spoken language while maintaining context, natural speech synthesis, and lip synchronization. These challenges lead to misinterpretations of idioms, loss of speech nuances, poor synchronization between speech and lip movements, and difficulties in noisy environments, especially for languages with distinct phonetic structures such as Telugu. To address these limitations, this study introduces a speech translation model from English to Telugu with integrated lip synchronization. The proposed system consists of two key components: speech translation and lip synchronization. The speech translation module leverages a Transformer-based architecture for high-accuracy English-to-Telugu translation, while the novel High-Quality Wav2Lip (HqWav2Lip) model ensures precise lip synchronization, contextual speech recognition, and natural Telugu speech generation. Our translation model achieves 91% text-to-speech accuracy for English-to-Telugu translation, outperforming existing state-of-the-art models. Furthermore, the HqWav2Lip model achieves a synchronization accuracy of 88%, demonstrating superior performance in the synchronization tasks of the Telugu lip. This work improves the realism and effectiveness of speech translation, contributing to more immersive and natural multilingual communication.</p>