In the rapidly evolving field of financial data analysis, integrating Artificial Intelligence (AI) is essential for enhancing predictive accuracy and decision-making processes. This study provides a comprehensive bibliometric review of AI applications in financial data analysis by analyzing 171 scholarly publications from the Scopus database spanning 1984 to 2024. Utilizing advanced tools like RStudio, Bibliometrix, and VOSviewer, we identified leading journals, influential authors, and key research clusters shaping the domain. Our findings highlight three primary research clusters: advanced machine learning techniques for improving financial strategies and predictions; AI models and optimization algorithms enhancing financial forecasting and decision support systems; and AI-driven approaches to financial risk evaluation and forecasting. Prominent journals such as Lecture Notes in Computer Science and Expert Systems with Applications have emerged as major contributors to the scholarly discourse. The study underscores the central role of AI-driven techniques like machine learning, risk assessment, and financial forecasting in advancing the field, while also identifying gaps in the literature and suggesting directions for future research. This bibliometric analysis serves as a critical resource for researchers and practitioners, offering a robust foundation for understanding the evolution of AI in financial data analysis. It paves the way for future scholarly exploration and practical innovations, guiding the integration of AI techniques to address the complex challenges of an increasingly data-driven financial landscape.