Artificial Intelligence (AI) is increasingly used in financial accounting to improve decision-making effectiveness. This research analyzes the role of AI in supporting data-driven decision making and identifies challenges in its implementation. Using a qualitative approach with the Systematic Literature Review (SLR) method, this study reviewed 41 relevant articles from national and international journals. The results showed that 28 studies supported the effectiveness of AI in improving financial decision-making by automating transaction recording, enabling algorithm-based predictive analysis, and detecting financial anomalies. AI enables companies to respond faster to market changes, increase transparency of financial reports, and reduce human errors in accounting processes.However, 13 studies highlighted challenges such as technological complexity, limited transparency in decision-making, algorithmic bias, and organizational readiness. In addition, evolving regulations are an obstacle to ensuring optimal use of AI while minimizing ethical and legal risks. The success of AI in financial decision-making depends on infrastructure readiness, regulatory support, and human resource competencies. Without a well-planned strategy, AI may pose new challenges that hinder its effectiveness. Therefore, this study provides insights into the optimal AI implementation strategy to ensure that this technology improves the accuracy and transparency of decision making while maintaining financial accounting accountability.