###### Abstract  
The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R\({}^{2}\)) reached 0.967, showing that the model's performance was impressively better than that of traditional fitting curves. uniaxial compression strength; support vector machine; fitting curves; prediction performance; limestone 2023330/app1304221730/11/202330/app1  
before failure [6]. UCS represents the compressive strength of a rock, which can be defined as the ability of the rock to resist forces imposed on it or the maximum stress that a sample can withstand under specified loading conditions. On the other hand, UCS directly informs geo-engineers about a rock's durability and its ultimate strength under different loading stages, providing essential data in the design stages to avoid structural failure and instabilities. This makes the UCS measurement very important in geo-engineering practice. ASTM D7012 [7] provides a description of the experimental procedure for the testing of samples. Estimating a rock's geomechanical index properties such as its UCS guarantees the success of design and construction assurance. So, accurate measurements are key points in evaluations. Traditional methods dictate that to achieve the appropriate level of accuracy, the number of samples should be high and repeatable (regarding the scope, sensitivity, and scale of the project) so that the average values can be calculated with less error. A low volume of samples will increase uncertainties and errors in the measurements. Using a large number of samples for a UCS test requires a long time period and is very costly. Thus, applying alternative methods that require a shorter period with less financial leverage that reaches a good accuracy is always welcomed. With the advances in computer-based algorithms, the geo-material strength indexes for different materials can be predicted and can achieve the expected level of reliability. Subsequently, many scientific researchers have launched various aspects of related research related to these predictions [8]. These studies have been implemented to complete the advancements of traditional procedures to estimate the geomechanical index properties of rock materials based on UCS, point-load [9], or Schmidt hammer [10] results. Recently, predictive techniques have changed significantly after the introduction of machine learning to geotechnical applications, especially material behavior predictions [11; 12; 13; 14]. [PERSON] et al. [15] and [PERSON] et al. [16] provided artificial neural network (ANN)-based predictive models for predicting several sedimentary and igneous intact rock index behaviors. [PERSON] and [PERSON] [17; 18] applied multilayer perceptron (MLP) as one of the most-common ANN-based predictive models to estimate geo-mechanical features indexes such as UCS based on UCS and point-load (Is) test results conducted on gypsum samples from Sivas in Turkey. [PERSON] et al.