A thorough bibliometric analysis of machine learning (ML) finance applications tracks the advanced technological development from 2010 to 2025 including evolving patterns and numerical patterns. The researchers utilized Scopus data to review the development of finance-related ML research while investigating main trends regarding authorship patterns and institutional affiliations and geographic origin of work. The research demonstrates that ML study has experienced substantial advancement throughout time especially since 2015 because of advancing computational power alongside increasing big dataset availability and advanced algorithm development. The research data demonstrates that the worldwide nature of ML exploration happens through major participation from both the United States and China and India. The outlined research topics in ML involve financial forecasting combined with risk management alongside algorithmic trading methods. This study generates important findings which guide both researchers and financial analysts while policymakers will find the results beneficial to understand ML capabilities in finance for contemporary uses. The analysis focuses on machine learning applications in finance to assess bibliometric research output along with its various financial forecasting methods and risk management systems combined with algorithmic trading concepts.