###### Abstract
This deficiency confirms that various properties of soils can differ greatly, even for those taken from the same place. Therefore,otechnical problems require learning strategies such as machine learning, which can take advantage of multiple predictors and their fundamental properties [31]. These complex situations can be handled with machine learning (ML). Machine learning has established an excellent field for itself in the geotechnical field, as it copes even with distorted, fuzzy, and incorrect data [32]. Various ML-based models are used for geotechnical problems, including machine learning methods for displacement estimation and prediction, as well as optimization studies in soil nailed walls. In a study, the structural risk minimization-multi-gene genetic programming (SRM-MGGP) method was proposed using support vector regression (SVR), artificial neural networks (ANNs), and multi-gene genetic programming (MGGP) methods to formulate the mathematical relationship between cohesion, friction angle, nail slope, nail length, and slope height, and to estimate the safety factor ([PERSON] et al., 2014). In another study, the optimum slope, spacing, and nail length/wall height of single-row vertical nails in soil nailed walls were analyzed using Plaxis 2D v.23. Then, a comparative optimization study was conducted with [PERSON]'s design of experiment (DOE), genetic algorithm (GA), and particle swarm optimization (PSO) [33]. To summarize, in the first stage of this study, the factors that may have an impact on the corner effect in soil nailed walls were investigated in many aspects. For this purpose, three-dimensional and two-dimensional models were created using Plaxis v.23 software, a finite element software program, and the performance of the model was compared with inclinometer measurements. Then, the effect of excavation pit dimensions on the corner effect was investigated. After the excavation pit dimensions were determined, the parametric research phase began. Analysis studies were carried out by creating 336 3D models and 336 plane-strain slice models with variables including slope angle (\(\beta\)), soil nailed wall angle (\(\alpha\)), nail length (L/H), and excavation depth (H), to determine the plane strain ratio (PSR). In the second stage of this study, PSR prediction was performed using machine learning methods. It is very difficult to make predictions with simple statistical methods, especially in studies with crowded data sets. Predictions made with machine learning methods can produce reliable results, especially in complex and large-volume data. As a result of this study, analysis studies were carried out with machine learning methods due to the large number of PSR data obtained and the non-linearity of the obtained results. There are many methods for machine learning. This is necessary because there is no method that works perfectly on every data set. For this reason, prediction studies have been carried out using more than one method. The methods that are most preferred in the literature provide reliable results. This study aims to reveal the most reliable results obtained from the predictions made by the methods. Artificial neural networks (ANNs), support vector regression (SVR), extreme gradient boosting (XGBoost), and classified and regression tree (CART) prediction models were used for PSR prediction, and their performances were compared using various metrics.