considerably for predicting the pile-bearing capacity [9]. The _HSDT_ is preferable to the _SLT_, because it operates with a faster, more advanced, and economic technology [2]. This quality supports its paramount importance addressed by the American Standards Test Methods to standardize the _HSDT_ method [1]. The literature on bearing capacity values revealed a relatively close accuracy in both the _HSDT_ and the _SLT_[1]. [PERSON] et al. [10] added that _HSDT_ is faster and more economic compared to SLT, but it generally requires several _HSDT_ tests for each project to obtain a reliable result [11]. Hence, increasing the number of _HSDT_ tests is extremely undesirable since it may increase the total project budget. Moreover, other empirical researchers have proposed traditional methods for estimating the bearing capacity [12; 13; 14; 15]. The quality of easiness and common usage has made these methods very important. However, determining the bearing capacity of bored and driven piles by means of the aforementioned methods is found to be time-consuming and costly [16]. This is probably due to the complex behavior of piles, heterogeneity of the soil around piles, material and shape of piles, and their installation. Accordingly, all the proposed methods/models in the literature yielded ineffective predictions [17]. On the other hand, currently, due to emerging new easy-to-use performance software such as PLAXIS, utilizing finite element analysis for which the system is discretized into a number of meshes to obtain axial capacity is of interest [18]. For this reason, numerical methods based on the finite element approach have recently become well-known for the evaluation of bearing capacity, yielding effective results [19; 20]. Recently, the application of some new advanced techniques, namely \"artificial intelligence (\(AI\))\" or \"machine learning (\(ML\))\", has witnessed a spectrum of interest, and they provided exceptional results in solving several issues by learning from the available data [21; 22]. Subsequently, the use of machine-learning methods to predict pile-bearing capacity has witnessed considerable development since the early 1990s [21; 22; 23; 24]. Several studies are now able to estimate the pile-bearing capacity with a higher degree of precision in comparison to traditional methods. Among the fundamental research dealing with the pile-bearing capacity, [PERSON] et al. have used one hidden layer of the _ANN_ model by investigating a database consisting of 25 test data. The chosen input parameters included the _SPT-N_ values and geometrical properties. The _ANN_ model efficiently predicted the pile-bearing capacity compared to traditional methods [25]. Furthermore, [PERSON] has predicted the pile-bearing capacity by using Support Vector Machines and Generalized Regression Neural Network with an input layer containing dynamic stress-wave data [26]. He concluded that the Generalized Regression Neural Network was the best model with a high correlation coefficient (0.977). In addition, [PERSON] et al. have developed an effective model based on Artificial Neural Network, genetic programming, and linear regression methods to predict the bearing capacity of piles by learning from 100 samples. They utilized the Flap number, basic properties of the surrounding soil, pile geometry, and pile-soil friction angle as an input layer. The suggested _ANN_ model has better stability compared to the other methods [27]. [PERSON] et al. used hybrid _PSO-ANN_ to predict the bearing capacity of rock-socked piles, by taking into consideration soil length to socket length ratio, total length to diameter ratio, uniaxial compressive strength, and standard penetration test. The proposed _PSO-ANN_ model has demonstrated its efficiency since it produced a high correlation coefficient (R = 0.9685) [1]. [PERSON] et al. have used _ANFIS_, _GP_, and _SA-GP_ for modeling a database consisting of 50 tests. The chosen input parameters included the pile length, pile cross-sectional area, hammer weight, pile set, and drop height.