Artificial intelligence (AI) and machine learning (ML) techniques for real-time language translation have become crucial field of study with significant ramifications for worldwide connectivity and communication. With an emphasis on the use of AI and ML techniques, this paper provides a thorough review of current developments and difficulties in the field of real-time language translation. We examine cutting-edge methods for language translation, such as cross-lingual transfer learning strategies, neural machine translation (NMT) models, and speech-to-speech translation systems. Real time language translation, literally means the translation of spoken or written text at the same time in one language into another, without boundaries. Based on advanced algorithms including such as natural language processing (NLP) and machine learning models, this technology allows the real time interpretation and translation of languages. Real time translation is used for international business meetings, customer support, travel and education. The problems are context, tone and rest of the things while keeping it on the speed and the accuracy of translations. As real time translation systems are made ever more reliable and in reach of a global audience, we see continual advancements in AI and computational linguistics.