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With the rapid growth of global digital communication, language barriers continue to limit accessibility and inclusivity in video-based content such as online education, live streaming, conferences, and media broadcasting. Traditional subtitle generation methods are often manual, time-consuming, and unsuitable for real-time applications. This research proposes an AI-Powered Real-Time Video Translation and Subtitling System that automatically generates and translates subtitles from live or recorded video streams. The system integrates advanced techniques including Automatic Speech Recognition (ASR) for converting speech into text, Neural Machine Translation (NMT) for multilingual translation, and Natural Language Processing (NLP) for improving contextual accuracy and grammatical correctness. Deep Learning models are also utilized for speaker identification and noise reduction to enhance transcription quality in real-world environments. The proposed system processes audio streams in real time, generates synchronized subtitles, and dynamically overlays translated captions onto video content with minimal latency. Additionally, it incorporates adaptive learning mechanisms to improve translation accuracy based on user feedback and domain-specific vocabulary. By enabling seamless multilingual communication, the system enhances accessibility, supports inclusive learning, and expands the global reach of digital content platforms. .