Finite element analysis modeling is a powerful technology to predict the response of materials and structures under certain loaded situations including the applied force, the changing temperature and humanity, the alterative boundary condition, etc. In this paper, the mechanical properties of wood material were analyzed with an emphasis on bending behavior under lateral applied force with the finite element simulation in ABAQUS (Dassault Systèmes, 2020 version). Two methods were conducted in ABAQUS commercial software and the modulus of elastic (MOE) attained from the computational results were compared with the data obtained from the experimental records. The simulation model with grain patterns into consideration showed more accurate behavior when comparing with the displacement from the 3rd point bending test during the elastic range. Machine learning method is widely applied to the image processing procedures like digital recognition. The paper developed a python script to process the wood image cross section with an environmental background and calculated the late wood proportion based on the unsupervised machine learning concept. Grab cut function and Gray Level Co-Occurrence Matrix (GLCM) image processing were defined to obtain the wood section and wood texture features separately. K-Means method was used to cluster the latewood and early wood material based on the mean value from the GLCM matrix then the script was able to calculate the ratio with a simple definition of the equation. The results of the latewood ratio from the python script were compared with the ratio from the dot grid method in this paper. Statistical models in SPSS version 27 (IBM, Chicago, IL) were taken for this paper to predict the relationship between several parameters quantitatively. Since the density, latewood ratio, and number of rings per inch are obviously correlated with each other, this paper proposed a ridge regression statistical model to study the relationship between MOE/modulus of rupture (MOR) with multiple independents. Ridge regression model is also known as Tikhonov Regularization method which aims to solve the collinearity problems that may lead to statistical bias with stepwise regression analysis.