Fluid Body is an experimental, body-driven instrument that maps the classical ballet movement vocabulary onto the playing of the Guzheng. An interactive machine-learning framework that combines classification and regression models to form an embodied sonification system is used. It learns correspondences between bodily dynamics and sound synthesis parameters, allowing participants to “play” a digital Guzheng through ballet postures. Based on granular synthesis, six ballet arm positions are associated with Guzheng pitch phrases, allowing continuous motion features to modulate sound texture in real time. The multi-modal feedback loop connects bodily movement and sound, transforming the body’s shape into soundscapes. Rather than viewing culture as a fixed heritage, Fluid Body presents performance as a process of intercultural co-adaptation. This work shows how machine learning can serve as a creative translator between different artistic traditions. It encourages a dialogue among bodily movement, sound synthesis, and cultural meaning, using common ideas of rhythm, modulation, and flow.