Liquid crystal elastomers (LCEs) are a relatively new class of materials that display soft elasticity, that is, they can be deformed without resistance. Furthermore, LCEs show a rapid and accurate response to external stimuli such as electric, magnetic, and thermal fields. For this reason, it is expected to be applied to actuators or sensors. In order to apply these characteristics to devices, we tried to predict the characteristics of LCE by simulation. First of all, we developed an extended coarse-grained LCE model to enable simulation of systems of various architectures. Our model is a hybrid of Gay-Berne particles and Lennard-Jones particles, based on previously reported LCE modeling techniques. By using molecular dynamics (MD) method, the stress-strain curves as the response to an external force were obtained, and soft elasticity was clearly observed. Then, the regression analysis using machine learning (ML) was conducted on the results of the stress-strain curves of the MD simulations. The results indicated that spacing for a room for mobility of mesogenic units in the design variables of LCE molecules affected elasticity. In addition, the R-squared value of regression curve for stress-strain curves was 0.821, which indicates a strong correlation between the MD data and ML results. Finally, the estimation method of molecular structure from coarse-grained model is discussed.