To address the need for efficient transportation of high-value-added and time-sensitive goods and resolve the challenge of selecting optimal freight paths within the complex urban rail transit networks of megacities, this study presents a comprehensive approach. First, a network simplification method is proposed to streamline the topological structure of urban rail systems by accounting for network complexity and uneven passenger flow distribution, thereby enhancing the computational efficiency of multimodal path optimization algorithms. Second, a path optimization model is developed with dual objectives of maximizing load rate and minimizing operational costs to balance cargo capacity utilization and economic feasibility. Finally, a hybrid algorithm integrating an improved ant colony optimization and particle swarm optimization is designed to solve the model, where particle swarm optimization optimizes the hyperparameters of the ant colony algorithm to accelerate convergence speed. Validated through a case study in Guangzhou, the methodology achieves significant performance improvements: network simplification reduces nodes by 76% and links by 69%, cutting algorithm runtime from 11,849 seconds to 1,613 seconds and boosting computational efficiency by 87.4%. The hybrid algorithm further reduces iterations from 46 to 15 while maintaining solution quality. Comparative analysis confirms the superiority of this approach in balancing computational speed, resource utilization, and path optimization effectiveness for urban rail freight systems.