Abstract Interval-censored survival data, where events are known only within observation intervals, require methods beyond standard right-censoring approaches. We assess the Expectation–Maximization (EM) nonparametric maximum likelihood estimator (Turnbull) against Kaplan–Meier (KM, pseudo right-censored) and parametric accelerated failure time (AFT) models. Simulations under Weibull and log-normal mechanisms show that EM achieves low integrated squared error (ISE), accurately recovering the true survival shape without distributional assumptions. Weibull AFT models, while assumption-dependent, yielded smoother curves and lower integrated Brier scores (IBS) when covariates were included. A real-world ovarian cancer example demonstrated EM’s robustness and Bayesian AFT’s ability to propagate uncertainty and perform posterior predictive checks. We propose a workflow: use EM to reveal baseline shape, then apply (Bayesian) AFT for covariate-adjusted prediction and uncertainty quantification.