Laser shock composite peening (LSCP) is one of the advanced methods for material surface enhancement, and it has gained significant attention recently due to its ability to induce beneficial residual stress fields. Traditional LSCP design methods involve selecting processing parameters through trial and error, which can be imprecise and time-consuming. These methods suffer from the complexities of internal stress wave transmission and non-uniform plastic strain under high strain rate loads. Machine learning (ML) algorithms offer a promising alternative by automating the design of critical LSCP parameters, thus reducing the iterative design process and associated costs. The work aims to leverage an Artificial Neural Network (ANN) algorithm to predict and regulate the residual compressive stress distribution on nickel-aluminum bronze surfaces, thus reducing the iterative design process and associated costs. An initial dataset of residual stresses in the spot overlap area was generated by an Abaqus finite element model with the Vdload subroutine and custom scripts. Before the regression analysis, the interquartile range (IQR) method was used to remove the top and bottom 10% of outliers, and the input data were standardized to eliminate the effect of data scale on the prediction results. The ANN model was trained and tested with the generated residual stress dataset, optimizing its hyperparameters for enhanced performance. Based on the process parameters in the training set, the residual stress values in areas prone to stress hole were predicted for 110 different process parameter sets. The results showed that almost all predictions were in close agreement with the actual values, confirming the strong prediction capability of the artificial neural network. The ANN accurately predicted residual compressive stress distributions, achieving an RMSE of 1.189 1, significantly outperforming other classical ML algorithms. The residual stress distributions were predicted and optimized, with the ANN model indicating compressive stress up to ?413 MPa across the treated surface. These predictions were validated by the test set, confirming the high prediction accuracy and robustness of the model against overfitting. Further analysis revealed that the predicted residual compressive stress distributions reached substantial effect depths, critical for material property enhancement. The LSCP process achieved a maximum efficiency of 1.87 mm2/s at a 1 Hz pulse repetition frequency. This method presents a novel approach to designing and regulating complex residual stress fields in LSCP, effectively addressing the challenge of residual stress hole in nickel-aluminum bronze. The integration of ML with LSCP not only optimizes the residual stress distributions, but also provides insights into the development of heterogeneous structures in the material due to non-uniform plastic strain. Future research will aim to incorporate more experimental data into the ML models to enhance their applicability to various types of metals and further refine the prediction accuracy of residual stress fields in complex material systems. This will involve collecting data from a broader range of experimental conditions and materials, thereby improving the robustness and versatility of the ML models. The goal is to create a comprehensive framework that can be applied to a wide array of materials and LSCP scenarios, ensuring that the benefits of this approach can be realized across different industries. This study contributes to the field of material surface enhancement by demonstrating the potential of combining advanced computational models with machine learning for more precise and efficient material treatment outcomes.