High-dimensional data, along with the time-to-event outcome analysis, is a challenging task. The accelerated failure time (AFT) model is an alternative to the Cox proportional hazard (CPH) model in survival analysis. However, there is a lack of available packages and functions to work on high-dimensional time-to-event data for the AFT model using Bayesian. We developed an R package 'afthd' that works with an advanced AFT model for high-dimensional time-to-event data under the Bayesian paradigm and also provides different diagnostics plots for univariate and multivariable Bayesian analysis. We attempted to present a computer code with open-source R software to work with high-dimensional data for the AFT model. The conventional AFT model has been extended for the Bayesian framework of log-normal, Weibull, and log-logistic AFT models for univariate and multivariable high-dimensional data contexts. The methodology is validated by performing the simulation technique, showing consistent results for parameters in all three types of parametric AFT models. The application part is also performed on two different real high-dimensional liver cancer datasets, which clearly reveals the proposed method’s significance by obtaining inferences for survival estimates for the disease. The developed package 'afthd' is competent to work with high-dimensional time-to-event data using the conventional AFT model and the Bayesian paradigm. Other aspects, like missing covariates in high-dimensional data and competing risks analysis, are also covered in this work.