Aiming at the limitations of low accuracy and poor stability in the transit time method for estimating carotid local pulse wave velocity, this paper proposed a machine learning-based local pulse wave velocity estimation method, which integrated carotid pulse wave time-domain features, age, and cardiac function parameters. The research was based on a dataset of carotid pulse wave propagation from 4 374 virtual subjects. By combining the Pearson correlation coefficient method and the least absolute shrinkage and selection algorithm to select multi-position combinations of pulse wave time-domain features, and integrating age, heart rate, and other parameters as input features, five machine learning models including multiple linear regression, Bayesian ridge regression, k-nearest neighbor regression, support vector regression and convolutional neural network were used to construct the carotid local pulse wave velocity estimation model, respectively. The results demonstrated that all five machine learning models showed higher accuracy and stronger stability than the traditional methods, and the support vector regression model achieved the optimal performance, with a normalized root mean square error of less than 1.80% and a coefficient of determination exceeding 0.980. In conclusion, it is hoped that the research results presented in this paper can provide a theoretical basis and technical support for the early quantitative assessment of local vascular elasticity of the carotid artery in clinic.