In recent years, in order to increase transmission capacity and optimize land resource utilization, the application of parallel cables in high-voltage power grids has become increasingly widespread. However, when multiple cables are running in parallel, the problem of current unbalance between cables gradually becomes prominent due to electromagnetic coupling, parameter differences, and laying methods, which directly affects the stability and safety of the system. Firstly, this article established a simulation model of a 500 kV parallel cable using ATP-EMTP software, simulating the current distribution characteristics under different laying methods and phase sequence arrangements, and quantifying the degree of unbalance. Based on simulation data, use the random forest algorithm to rank the weights of influencing factors. Subsequently, in order to improve the accuracy of unbalanced prediction, this article compared various machine learning algorithms and ultimately determined Bayesian regularization algorithm as the optimal prediction model by comparing the prediction results. This article provides theoretical basis and technical support for improving the transmission efficiency, operational stability, and resource utilization of high-voltage power grids by establishing a simulation model of 500 kV parallel cables, analyzing the factors affecting current unbalance, and optimizing prediction algorithms.