Automatic dubbing (AD) aims to replace the original speech in a video with translated speech that maintains precise temporal alignment (isochrony). Achieving natural synchronization between dubbed speech and visual content remains challenging due to variations in speech durations across languages. To address this, we propose an end-to-end AD framework that leverages large language models (LLMs) to integrate translation and timing control seamlessly. At the core of our framework lies Duration-based Translation (DT), a method that dynamically predicts the optimal phoneme count based on source speech duration and iteratively adjusts the translation length accordingly. Our experiments on English, Spanish, and Korean language pairs demonstrate that our approach substantially improves speech overlap—achieving up to 24% relative gains compared to translations without explicit length constraints—while maintaining competitive translation quality measured by COMET scores. Furthermore, our framework does not require language-specific tuning, ensuring practicality for multilingual dubbing scenarios.