One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.