Abstract: Research on the inverse identification of constitutive parameters and mechanical properties of human facial skin plays a crucial role in early diagnosis of skin lesions, design of biomimetic materials for skin, and establishment of facial models in computer graphics. The combination of machine learning and finite element simulation methods can provide a more efficient and accurate solution to the non-invasive inverse problem of skin tissue constitutive parameters. In this study, a finite element model of facial skin under multi-directional stretching was established, and the stress relaxation behavior of skin was separated into hyperelastic and viscoelastic mechanical properties using the multi-step displacement control method. The sensitivity analysis of the Gasser-Ogden-Holzapfel (GOH) and Prony series constitutive model parameters was conducted to reveal the key parameters that affect the results of facial skin stress relaxation experiments. Furthermore, by utilizing Bayesian hyperparameter optimization theory, a random forest (RF) model and a support vector regression (SVR) model were constructed to inversely determine the constitutive parameters of human facial skin tissue based on experimental data. Finally, the computed finite element simulation curves were compared with the experimental stress-strain response curves, and the coefficient of determination (R²) was introduced to evaluate the predictive accuracy of the two models. The results showed that fiber tissue dispersion coefficient κ, shear modulus related parameters C₁₀, and relaxation modulus g₁ were the key parameters influencing the results of skin stress relaxation experiments. The RF model achieved a goodness of fit of 0.98 between the numerical computation curve and the experimental curve, demonstrating higher accuracy in the inverse identification of skin constitutive parameters. Machine learning can accurately and efficiently obtain the constitutive parameters of facial skin, thus accurately describing the mechanical properties of skin tissue. This method can also be further extended to the complex inverse problem of constitutive parameter identification in other biological soft tissues.