Echocardiography is essential for cardiovascular disease detection, but it usually suffers from a heavy reliance on experienced sonographers. To address this, the echocardiography probe guidance system, which predicts real-time movement instructions for acquiring standard plane images, has emerged as a promising technique for enabling fully autonomous or AI-assisted echocardiography scanning.However, it poses unique challenges in developing proper machine learning models, which have rarely been explored in existing studies.In particular, an ideal guidance model needs to comprehend both the heart’s structural anatomy and the dynamic changes resulting from probe movements, while integrating historical visual-motion signals into the decision-making process.In response to these issues, this paper presents EchoWorld, a motion-aware world modeling framework for probe guidance that encodes anatomical knowledge and motion-induced visual dynamics, while effectively leveraging past visual-motion sequences to enhance guidance precision. EchoWorld employs a pre-training strategy inspired by world modeling principles, where the model predicts masked anatomical regions and simulates the visual outcomes of probe adjustments. Built upon this pre-trained model, we introduce a motion-aware attention mechanism in the fine-tuning stage that effectively integrates historical visual-motion data, enabling precise and adaptive probe guidance. Trained on more than one million ultrasound images from over 200 routine scans, EchoWorld effectively captures key echocardiographic knowledge, as validated by qualitative analysis. Moreover, our method significantly reduces guidance errors compared to existing visual backbones and guidance frameworks, excelling in both single-frame and sequential evaluation protocols. Our code will be released after acceptance.