###### Abstract
[19] applied the same technique (MLP) to predict the UCS values from samples taken from Misis Fault Breccia in Nigde, Turkey. The results were used for a deformability evaluation in the studied site. [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22] applied several types of machine learning classifiers to evaluate and predict UCS variations compared to regression models, which indicated that the predictive model showed a good performance in estimating the indices. The coefficient of determination (R\({}^{2}\)) is the main criterion used to estimate predictive performances. [PERSON] et al. [23] and [PERSON] et al. [24] applied MLP, support vector machine (SVM), and other benchmark algorithms to predict the UCS values of shale and granite samples utilized for predicting geomechanical index behavioral evaluations. [PERSON] and [PERSON] [25] used the same neural network to identify the UCS changes in compacted claye soils as stiff materials. Their results were incredibly close to the actual data during the prediction process. [PERSON] et al. [26] developed a forecasting model to obtain the relationship between UCS and the elastic modulus (E) in sedimentary rocks, which was appropriately used to estimate the correlation for different types of sediment units by using regular regression analysis. [PERSON] et al. [12] used the coupled MLP with ordinary multivariate regression analysis to implement a predictive model for masonry samples taken from Jordan to predict the UCS variations in the materials. Using computational techniques such as machine learning improves the reliability of predictive models with reasonable accuracy and low errors, according to the literature cited. Regarding these claims, regression analysis is used to establish a correlation between the UCS values from the predictive model and the measured data and the prediction models created by the machine learning classifiers. This link can further enhance the prediction accuracy of forecasting the UCS of samples. To this end, the presented study attempted to use one of the most-efficient benchmark machine learning algorithms, SVM, to analyze and predict UCS variations in sedimentary rocks from the Maragheh region in Iran. SVM-based models are supervised learning models with associated algorithms for classification and regression analysis. The SVM method works relatively well when there is a clear margin of separation between classes. It is more effective in high-dimensional spaces and cases where the dimensions are greater than the number of samples. In addition, SVM is relatively memory efficient and is widely used in classifications related to forecasting procedures [27]. The presented study used these advantages to prepare a predictive model for analysis and predict UCS variations in Maragheh limestone, which was mainly implemented in a Python high-level programming environment [28]. The main objective of this article was to provide a reliable model to classify and predict the UCS values for limestone-type sedimentary rocks from the Maragheh region. Given that the traditional and usual methods of conducting extensive tests and using experimental regression methods to determine the changes in the resistance of rock masses are costly and time-consuming, methods that effectively reduce the cost and increase the speed of analysis have recently gained the attention of rock engineering experts. In this regard, computer models and artificial intelligence can be used to estimate the resistance parameters of rock masses, especially UCS. As an achievement, this is of interest to geo-engineers. So, the presented study attempted to use an SVM-supervised model to classify and predict the UCS of rock samples from the studied region.