Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. This study covers the fundamentals of blockchain and machine learning and discusses their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved. Keywords: Blockchain, Machine learning, Internet of things, Supply chain, Medicine, Finance, Security Blockchain and machine learning have the potential to create a secure, decentralized, smart, and effective network transaction and administration system. Both academia and industry have shown great interest in the benefits that this combination brings, such as improved information and model contribution, enhanced security and confidentiality, and reliable decision-making in machine learning. ML is assumed to have a substantial effect on the advancement of blockchain in communication and networking systems by increasing efficiency, scalability, and security. This combination enables the secure and transparent storage of large datasets, allowing machine learning models to access and train on reliable data. Also, it enhances data privacy and control by providing decentralized ownership and permissioned access. Lastly, it enables the development of decentralized machine learning models, allowing participants to contribute their computational resources while maintaining data privacy, leading to more collaborative and efficient machine learning ecosystems. The purpose of this study is to highlight the areas where blockchain and machine learning are used together. We completed our literature search on Feb 15th, 2023 with the referenced papers. The rest of the paper is organized as follows. "Blockchain technology" provides an overview of blockchain with explanations of Ethereum, smart contracts and consensus algorithms. "Machine learning" describes machine learning. "Literature review" provides the literature overview on the integration of these two technologies, along with their contributions, gaps, and advantages in the fields of finance, medicine, supply chain, and security. "Real world examples" gives some real-world examples for blockchain and machine learning integration. Finally, "Conclusion" concludes the article with key highlights, comments and future trends.Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. This study covers the fundamentals of blockchain and machine learning and discusses their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved. Keywords: Blockchain, Machine learning, Internet of things, Supply chain, Medicine, Finance, Security Blockchain and machine learning have the potential to create a secure, decentralized, smart, and effective network transaction and administration system. Both academia and industry have shown great interest in the benefits that this combination brings, such as improved information and model contribution, enhanced security and confidentiality, and reliable decision-making in machine learning. ML is assumed to have a substantial effect on the advancement of blockchain in communication and networking systems by increasing efficiency, scalability, and security. This combination enables the secure and transparent storage of large datasets, allowing machine learning models to access and train on reliable data. Also, it enhances data privacy and control by providing decentralized ownership and permissioned access. Lastly, it enables the development of decentralized machine learning models, allowing participants to contribute their computational resources while maintaining data privacy, leading to more collaborative and efficient machine learning ecosystems. The purpose of this study is to highlight the areas where blockchain and machine learning are used together. We completed our literature search on Feb 15th, 2023 with the referenced papers. The rest of the paper is organized as follows. "Blockchain technology" provides an overview of blockchain with explanations of Ethereum, smart contracts and consensus algorithms. "Machine learning" describes machine learning. "Literature review" provides the literature overview on the integration of these two technologies, along with their contributions, gaps, and advantages in the fields of finance, medicine, supply chain, and security. "Real world examples" gives some real-world examples for blockchain and machine learning integration. Finally, "Conclusion" concludes the article with key highlights, comments and future trends.