In response to the shortcomings of traditional enterprise financial management information platforms in data processing and analysis efficiency and decision support capabilities, this study introduces intelligent decision support systems to fundamentally improve these issues. In this study, we automated data collection through API (Application Programming Interface) technology, used ETL (Extract, Transform, Load) tool for data format conversion, and strictly performed data cleaning and standardization to ensure data quality. The article uses association rules and support vector machine machine learning algorithms for in-depth analysis and prediction of financial data, and optimizes decision-making scenarios based on multi-criteria decision analysis, Monte Carlo simulation and linear programming techniques. Evaluation results show that the system significantly improves the speed and accuracy of data processing, with an increase in processing efficiency of more than 70% and a decision-making accuracy rate of up to 95%. The intelligent decision support system effectively improves the informatization level of enterprise financial management and provides more scientific and reliable decision support for the enterprise leadership.