###### Abstract
Based on historic events, these prediction and mapping approaches can identify and locate the probability of occurrences of such events in a particular region with similar environmental, geophysical, geological, and other related conditions [15]. Although research on LSM is not capable of stopping landslide hazards totally due to its complex nature, highly precise LSM can help in reducing the impacts of this hazard by developing effective mitigation policies and measures [16; 17]. In order to generate high-precision susceptibility maps, machine learning (ML) and data-mining approaches have attracted a number of researchers, particularly since 2005, and the development of ML-based quantitative methods has proven to provide accurate and reliable results of landslide susceptibility [18]. Based on the assumption of certain geological, geotechnical, and climatic conditions exhibiting similar patterns of landslide [19], these methods are considered as most effective in solving the nonlinear nature of geospatial problems. Numerous studies have discussed the advantages of different ML methods including logistic regression (LR) [20], artificial neural network (ANN) [21], support vector machine (SVM) [10; 21; 22], decision trees (DT) [22], maximum entropy (maxENT) [11], random forest (RF) [23; 24; 25; 26; 27], and boosted trees (BT) [28]. A comprehensive review of the architecture and working mechanisms of popular ML methods can be found in [PERSON] et al. [29]. Several efforts have been made to implement, investigate and compare these ML methods in different geographical settings [10; 15; 22; 23; 24; 25; 30; 31]. For example, [PERSON] et al. [32] investigated and compared the performances of RF, gradient-boosting machine (GBM), SVM, neural network (NNET), and LR in Mila Basin, Algeria. The authors of this study found that the GBM and RF methods provided good results, with areas under the curve (AUCs) of 0.897 and 0.895, respectively. On the other hand, a similar assessment was conducted by [PERSON] et al. [33] in Shexian County, China, using the RF, SVM, GBM, multilayer perceptron (MLP), and LR models and found that RF and SVM provided the results with the best AUCs of 0.821 and 0.803, respectively. Recently, [PERSON] et al. [34] implemented multiple ML models including generalized linear models (GLM), SVM, Naive Bayes (NB), and other tree-based models to study the susceptibility of a debris flow along the China-Pakistan Karakoram Highway. The authors adopted different modeling strategies based on the watershed and catchment boundaries in the peripheries of the highway and found that the SVM performed best (AUC = 0.96). [PERSON] et al. [35] assessed the prediction accuracies and performances of NB and RF by comparing their results with a fuzzy decision-making trial and evaluation laboratory approach combined with the analytic network process approach (FDEMATEL-ANP) in Kysuca river basin, Slovakia. RF was found to be an optimal model with an AUC value of 0.954 in the region. As a conclusion to the studies published so far, the accuracies of ML algorithms vary under different conditions of topography, geological setting, and climate [20]. Regardless of the number of ML methods, there is _'no rule of thumb'_ and _'no free lunch'_ [36] regarding which method or technique is the most suitable for landslide susceptibility due to the high level of uncertainty behind the processes and the varying topographical and climatic conditions of areas [37]. Therefore, it is necessary to study landslide dynamics and proneness by testing these algorithms for different site conditions for effective risk management and planning. In the current study, we adopted five ML algorithms, RF, SVM, maxENT, GBM, and LR, to study the landslide susceptibility at the regional level in deep canyons and rugged terrain of northern Pakistan, where slopes vary up to 89 degrees. The selection of these five ML models was based on several reasons.