Ground current in high-voltage cable systems serves as a critical indicator for ensuring operational safety and stability. Accurate ground current prediction is crucial for fault prevention and enhancing grid reliability. To address the limitations of traditional time-series prediction models in terms of prediction accuracy and computational efficiency, this paper proposes a ground current prediction model based on the Mamba architecture, referred to as the Bi-EMamba model. Through a spatiotemporal dependency encoder, the model effectively captures long-term dependencies and spatial correlations in multivariate time series while maintaining high memory efficiency. To address the challenge of non-stationary data, the model incorporates Reversible Instance Normalization for data normalization and employs hyperparameter optimization to further improve prediction accuracy and generalization capability. Experimental results based on a dataset from a high-voltage cable line in Beijing demonstrate that Bi-EMamba outperforms existing benchmark models across various prediction horizons. Notably, in long-term forecasting scenarios, it exhibits superior generalization and computational efficiency. Compared to the current state-of-the-art model, iTransformer, Bi-EMamba achieves a 6.52% reduction in Mean Squared Error, a 3.21% reduction in Mean Absolute Error, and a 29.49% reduction in memory usage.