Since mankind has always attempted to discover new materials, the ML revolution in the discovery of new materials with novel properties has led to its popularity in materials science [3]. ML has been used in the prediction of new compositions of HEAs with optimal properties [4]. An artificial neural network model has been proposed to predict the evolution of the eutectic HEA compound phase and the properties of the materials that it can stretch and deform. Eutectic HEAs are a homogeneous mixture of materials that melt or freeze at a unit temperature and form crystals simultaneously [5; 6]. The artificial neural network method was used to identify a distinguished type of VEC, which is the agent for the stability of HEA phases. The ML perspective is data-driven, which is clustering and classification. Since the ML methods can learn, they can improve their accuracy in the face of unseen data over time [7]. Hence, ML has been become one of the principal technologies in materials informatics. The ML methods in phase prediction for HEAs are a proper alternative in terms of accuracy to traditional predicting methods. Another ML application is in metamaterials, the properties of which are not due to their chemistry, but rather to their shape and structure [8]. Based on a study performed by [PERSON] and [PERSON] [9], the neuro-fuzzy interface system was used to predict HEA phases by two types of inputs. The first one is elements of composition, and the second one is HEA descriptors. The descriptors \(\delta\), \(\Delta H_{mix}(\mathrm{kJ}/\mathrm{mol})\), \(S_{c}(k_{B}\;per\;atom)\), \(\varphi\), \(\varepsilon_{RMS}\), and VEC provide good insights into phase formation. [PERSON] et al. [10] reported six different ML algorithms to predict the multiphase of eutectic HEAs which were considered the BCC and FCC phases in two different categories of solid solutions and inter-metallic solid solutions. The authors of [11] proposed a new method called MOFSocialNet. They created a social network based on a metal-organic framework (MOF). SNA tools have been used to extract groups of chemical MOF compounds named communities which have mostly similar functionalities. MOFs are different from HEAs in terms of descriptors, elements, and functionality. Based on another study conducted by [PERSON] et al. [12], ML strategies were used to identify useful HEAs. This model used ML methods to find high-hardness alloys, which are feature-oriented. The ML method was trained to map between hardness and descriptors. The strength efficiency at the desired temperature was predicted using the regression method of random forest [13]. The model was used to select related HEA features and eliminate irrelevant properties. Based on another study performed by [14], a model was proposed to predict the solid solution strength or hardness of HEAs. It was based on ML feature selection and feature structures. The development of HEAs included phase formation and microstructures, different descriptors, and multiscaling [15]. A deep-learning-based model was proposed for HEA phase prediction, which optimized the hyper parameters through the Bayesian technique [16]. Based on another study conducted by [PERSON] et al. [17], the gradient boosting model was proposed to determine a single-phase solid solution and non-single phase solid solution alloys. The proposed method was used to discover new metallic materials of HEAs. The correlation of experimental parameters while moving from medium to high entropy with a solid solution phase was focused on [18]. In our previous study [19], HEA descriptors were utilized to construct an HEA interaction network. Initially, HEA descriptors were investigated, and that each HEA composition has its own phase.