Machine translation has become a fundamental principle of global communication which is driven by the need for accurate, scalable, and efficient translation systems. Traditional statistical and rule-based methods have been replaced by neural network architectures, in particular, sequence-to-sequence (Seq2Seq) models with attention mechanisms. However, difficulties such as scalability, latency, and domain adaptability continue in production environments. This paper introduces a novel neural network-based approach which is optimized for production-scale machine translation.Our system achieves state-of-the-art translation quality by leveraging transformer architectures and integrating techniques such as model pruning, quantization, and distributed training while maintaining low latency and high throughput. Experimental results demonstrate significant improvements in BLEU scores across multiple languages and robust performance in real-world scenarios.