This article introduces the technical implementation of Ethereum and proposes a solution to apply machine learning to the Ethereum network to enhance security, efficiency, and scalability. Innovations have been made in Ethereum's transactions, consensus mechanisms, signature algorithms, data storage, and execution architecture. Machine learning can be applied to Ethereum for optimizing transaction processing, smart contract security, user segmentation, and network stability. Models like RFM and algorithms like DBSCAN can help identify high-value users and customize financial services. In the future, Ethereum can develop more complex machine-learning applications to improve network efficiency and security, and even achieve AI-driven governance mechanisms.