## 1 Introduction
While the depth from the surface and Almen density were used as input values in the study, hardness and compressive residual stress values were used as outputs. At the end of the study, it was determined that there was a positive correlation between compressive residual stress and hardness and Almen intensity. [PERSON] et al. [23] looked into ways to modify SP parameters to produce particular residual stresses that would lessen blade deformation. The equivalent residual stress can be calculated using the cosine function that characterizes the depth distribution of this stress. The study also investigated the effects of SP process parameters such as nozzle speed, flow rate, distance, and air-pressure velocity on the equivalent residual stress. In their study, the authors determined the effect of equivalent residual stress on wing deformation by developing a finite element model by simulating the deformation occurring in the wing. Using the inversion of the exhaustive approach, they obtained deformed stress fields of less than 0.03 mm. In order to determine the optimal SP parameter and validate the accuracy of the optimization procedure, SP experiments were employed. In a prior work, [PERSON] et al. [24] applied SP, severe vibration peening (SVP) and laser shock peening (LSP) methods to V-notch AlSi1 Mg samples produced by the laser powder-bed-fusion method. Then, they attempted to determine the relationship between the residual stress, surface roughness, the depth of the crack initiation zone, and the fatigue behavior of these samples using experimental data, numerical models, and deep learning approaches. They utilized a stacked autoencoder (SAE) to pre-train the dataset and a six-layer deep neural network (DNN) to create the machine learning (ML) model. By utilizing SAE, the authors were able to obtain an accuracy in the projected findings at the end of their investigation that was higher than 0.96. The authors concluded that all applied SP treatments resulted in the decreased surface roughness of the specimen, surface modification, surface-layer hardening, and significant compressive residual stress formation. All changes also resulted in significant improvements in fatigue life. In addition, when the neural network model developed for the study was evaluated, it was determined that the R\({}^{2}\) value was 0.96 and this value was within an acceptable range. The authors stated that one of the most important results of the study was that the subsurface crack initiation depth caused a significant increase in fatigue life. Analytical investigations took the role of experimental studies, which incur large time and financial costs. Therefore, the machine learning method's prediction helps to support experimental studies. Looking at the earlier research, it can be seen that machine learning techniques were mostly used to estimate the parameters of the SP process using a single material and one method. Unlike previous studies, this one employed various machine learning algorithms to forecast various SP process parameters and forging techniques for various steel types. The residual stress, surface roughness, and surface hardness values of four distinct materials that were shot-peened under various process conditions were ascertained for this purpose. A comparative analysis was performed on the data obtained before and after the SP operation. In the current study, this was accomplished by using the following methods: deep neural network (DNN), AdaBoost Regressor (ABR), artificial neural network (ANN), Decision Tree Regressor (DTR), Gradient Boosting Regressor (GBR), Random Forest Regressor (RFR), Support Vector Regressor (SVR) and XGBoost Regression (XGBR). Therefore, the goal was not only to identify the most efficient technique but also to make a determination using multiple estimation techniques. Furthermore, these studies were used to estimate the best SP parameter using the DNN and XGBo approaches, respectively.