This paper proposes a big data- and machine learning-based optimization approach toward the financial resource allocation problem with a view to enhancing efficiency and rationality in financial resource allocation. Consequently, an integrated system with big data analytics and machine learning algorithms was developed to achieve efficient financial resource allocation, thus decisively enhancing liquidity and the utilization of resources. A hybrid algorithm extended from the random forest to particle swarm optimization initializes feature selection, optimizes the parameters, and enhances the accuracy and convergence speed of the model, effectively reducing execution time and resource consumption. Experimental results reveal that several performance indicators of accuracy, recall rate, and F1 score for the optimized model demonstrate significant improvements compared to the basic model. In the meantime, with the intelligent prediction and risk management function, the rationality of fund allocation and the overall stability of the financial system are further enhanced. The system architecture will adopt module design for easy expansion and maintenance in future, laying a solid foundation for the realization of more intelligence in financial resource allocation. This research offers an effective method to make the best use of the financial resource and also demonstrates the great potential of big data and machine learning in finance.