The article "Blockchain meets machine learning: a survey" by Safak Kayikci and Taghi M. Khoshgoftaar explores the integration of blockchain and machine learning technologies and their applications in various industries. Blockchain, a secure and transparent decentralized ledger, and machine learning, which enables data-driven decision-making, are combined to enhance efficiency, security, and privacy. The study covers the fundamentals of both technologies and their integration in finance, medicine, supply chain, and security. Key contributions include increased security, privacy, and decentralization. However, challenges such as security issues, strategic planning, information processing, and scalable workflows remain to be addressed. The article also discusses real-world examples and concludes with key highlights and future trends. In finance, blockchain and machine learning improve efficiency and security through automated processes and data-driven decision-making. In medicine, they enhance patient privacy and data security, improve clinical trial efficiency, and support drug supply chain management. In supply chain, they increase transparency, reduce fraud, and optimize processes. Despite these advancements, standardization and interoperability are crucial for widespread adoption.The article "Blockchain meets machine learning: a survey" by Safak Kayikci and Taghi M. Khoshgoftaar explores the integration of blockchain and machine learning technologies and their applications in various industries. Blockchain, a secure and transparent decentralized ledger, and machine learning, which enables data-driven decision-making, are combined to enhance efficiency, security, and privacy. The study covers the fundamentals of both technologies and their integration in finance, medicine, supply chain, and security. Key contributions include increased security, privacy, and decentralization. However, challenges such as security issues, strategic planning, information processing, and scalable workflows remain to be addressed. The article also discusses real-world examples and concludes with key highlights and future trends. In finance, blockchain and machine learning improve efficiency and security through automated processes and data-driven decision-making. In medicine, they enhance patient privacy and data security, improve clinical trial efficiency, and support drug supply chain management. In supply chain, they increase transparency, reduce fraud, and optimize processes. Despite these advancements, standardization and interoperability are crucial for widespread adoption.