To improve the global optimization capability of submarine cable routing planning algorithm, reduce cumulative costs and risks, and improve algorithm efficiency, a machine learning assisted (MLA) multi-objective optimization algorithm for submarine cable routing planning is proposed. Utilizing the advantages of reinforcement learning, MLA autonomously iterates learning, synchronously optimizes costs and risks, considers parameters such as seabed topography and water depth, and adopts Pareto frontier as the convergence evaluation criterion. It is compared and verified with traditional ant colony optimization (ACO) algorithm. The experimental results show that under the same risk level, the algorithm can reduce the laying cost by 27.45%, and its optimal solution cumulative risk is only 25% of the ACO algorithm, and the convergence speed is improved by more than 330 times. In addition, most of its Pareto solutions are located at the forefront, which is significantly better than the discrete distribution of the ACO algorithm solution set.