PURPOSE OF REVIEW: Estimating the effect of environmental mixtures on survival outcomes is common in epidemiological studies, yet the applicability and performance of advanced mixture modeling methods in this context remains underexplored. In this review, we identify available methods for this context and evaluate their performance via simulations. RECENT FINDINGS: We compared five methods – Cox Proportional Hazards (with/without penalized splines), Cox Elastic Net, Bayesian Additive Regression Trees (BART), and Multivariate Adaptive Regression Splines (MARS). Simulations showed log-linear models achieved low coverage when estimating individual exposure and mixture effects, especially under high exposure correlations and proportional hazards violations. More flexible models exhibited higher variability but improved coverage in effect estimation. While flexible models were better able to estimate mixture effect on survival outcomes compared to more constrained models for most simulation scenarios, they still introduced bias and often had high variability. Given real-world constraints like limited sample sizes and high censoring, there likely remains significant complexities for the application of flexible modeling for environmental mixtures for the survival analysis contexts. We recommend evaluating if findings are consistent across methods.