This paper focuses on enhancing the prediction of vertical soil displacement during deep excavation using Artificial Neural Networks (ANN). Precise prediction of soil movement is essential to ensure the stability of the construction site and surrounding infrastructure. Traditional methods, such as the Finite Element Method (FEM), while accurate, are time-consuming and computationally intensive. In contrast, ANN offers fast and reliable predictions, making it a valuable tool for real-time decision-making. This study integrates FEM-based data to train the ANN model, ensuring the ANN captures complex, non-linear interactions between input variables like depth, pore water pressure, and coordinates. The model is trained and evaluated using performance metrics such as MAE, MSE, RMSE, and R2. With a high correlation coefficient R2 = 0.969238, the ANN model provides predictions with minimal error, demonstrating its effectiveness in replicating real-world measurements. The combined approach of ANN and FEM leverages the strengths of both methods, with FEM offering detailed physical insights and ANN optimizing computational efficiency. The results indicate that ANN-based models can serve as an efficient predictive tool in large-scale construction projects, improving safety by anticipating potential soil displacement issues. Future research will focus on expanding the model's applicability across different soil conditions and enhancing prediction capabilities with other machine learning algorithms.