This review paper explores the intersection of machine learning algorithms and financial markets. It begins with an overview of financial markets’ com-positions, emphasizing their importance in the global economy. Subsequently, it delves into various machine learning algorithms commonly applied in financial market prediction, including linear regression, decision trees, support vector machines, neural networks, and more. Each algorithm’s principles, strengths, and applications are discussed. Furthermore, the paper provides summaries of recent research papers concerning the utilization of these algorithms in financial market prediction, highlighting their methodologies, findings, and implications. Finally, future directions and potential improvements in algorithmic approaches to financial market analysis are suggested.