The mechanics of soil deal with soil types whose behavior in the real world can be erratic, including expansive soil, particularly black cotton soil, for which a variety of laboratory tests are required to establish its physical properties. Modelling the behavioral patterns of expansive-soil is difficult and often away from the capabilities of most traditional physical-based engineering measures. In comparison with conventional approaches, artificial intelligence-based approaches provide better predictions of the complex soil properties. The bearing capacity of soil is considerably very important for geo-techniques and engineering geology, since it is a significant parameter for the foundation and pavement's design. To improve granule connections and reduce soil expansibility and contractility, the expansive black cotton soil(BCS) is chemically Processed with a variety of supplements such as fly ash, lime, and cement in this study. By estimating the effects of stabilizers on CBR and UCS, this study tests the suitability of stabilizing black cotton soil in road construction. The second goal is to create an ANN-based model for predicting CBR and UCS while taking other soil properties as inputs. The research clearly illustrates that ANN Pattern is well suited to dealing with expansive soil stabilization, as evidenced by a high R2 and low RMSE, MAE and RRMSE. As a result of this paper, practicing engineers will be able to find the best modelling techniques and generate the necessary data to resolve soil mechanics problems using ANN.