Reaching the optimum moisture content and maximum dry density in a laboratory setting, using both standard and modified compaction tests, demands significant resources. It involves a considerable amount of time (typically 2-3 days), substantial effort, and a large volume of soil (around 20 kg per individual test) [1]. Efforts have been made to overcome this challenge through proposing several prediction models for determining the soil compaction parameters more efficiently. Empirical equations, created via multiple linear regression, can predict \(w_{opt}\) and \(\rho_{\text{dmax}}\) of soil [20; 21; 22; 23; 24; 25]. These models offer the benefits of quick calculations and easy application, which speed up decision-making in geotechnical projects. They are user-friendly, making them accessible to a variety of users, not just experts. However, these models come with several limitations. One issue is the existence of numerous equations for the same factors, leading to potential confusion and inconsistency in results. This vast array of options makes the selection of the appropriate model challenging, possibly affecting the reliability of predictions. Another significant limitation is the assumption of linear behavior, which often does not reflect the complex, often non-linear nature of soil behavior. These models, while providing an estimation, might not fully encapsulate the range of outcomes in different conditions due to their simplified assumptions. The complex nature of soil compaction parameters, influenced by numerous interrelated parameters, necessitates the use of robust, data-driven methodologies such as artificial intelligence. Traditional models struggle to capture these intricate relationships, but artificial intelligence and other data-driven methods have shown proficiency in navigatingthe inherent non-linearity and successfully estimating soil compaction parameters. Continued research should focus on employing precise and reliable techniques, like artificial intelligence, that can effectively handle these complexities. In the area of predicting soil compaction parameters, several studies have demonstrated noteworthy advancements. The use of artificial neural networks (ANNs) has been successfully employed in various models. For instance, one investigation applied an ANN to model \(w_{opt}\) and \(\rho_{\text{dmax}}\) in soil stabilized with nanomaterials. The model exhibited impressive prediction accuracy of over 97%, surpassing conventional statistical models [26]. Extending this approach to different soil types, another study harnessed the potential of deep neural networks to predict soil compaction parameters specific to Egyptian soil. The study achieved remarkable accuracy, with R\({}^{2}\) values of 0.864 for \(\rho_{\text{dmax}}\) and 0.924 for \(w_{opt}\), thereby surpassing the performance of a simple artificial neural network with just one hidden layer [27]. In the context of highway projects, a multi-layer perceptron neural network model was developed to predict modified compaction parameters for both coarse and fine-grained soil samples. This model, similar to its predecessors, delivered highly accurate results [28]. Stepping beyond traditional ANNs, a novel hybrid intelligence paradigm known as the ANFIS-IGWO model was proposed in a different study. This model demonstrated superior precision in predicting soil compaction parameters, boasting correlation values of 0.9203 and 0.9050 for \(w_{opt}\) and \(\rho_{\text{dmax}}\), respectively [29]. This accuracy was found to surpass other hybrid ANFIS models. In yet another study, an ANN was utilized to develop predictive equations for the Proctor compaction parameters of fine-grained soil [30]. The resulting ANN model demonstrated high efficiency and accuracy, particularly in predicting unseen datasets, thus setting a new standard compared to existing models in the literature.