Propensity score (PS) methods, including inverse probability of treatment weighting (IPTW) analysis, are increasingly applied to complex survey data in geriatric studies to infer causal effects. However, the comparative effectiveness of various PS estimation methods, particularly novel machine learning algorithms, has not been thoroughly explored when complex survey data are involved. We conducted a comprehensive simulation study to compare the following six PS estimation methods in IPTW analysis: Logistic Regression, Covariate Balancing Propensity Score, Generalized Boosted Model, Classification and Regression Tree, Random Forest (RF), and Super Learner. We considered 12 scenarios with varying treatment effects, degrees of non-linearity and non-additivity in the associations between covariates and the exposure, and levels of PS overlap. The performance of these six methods was assessed in terms of mean relative bias, root mean square error, and coverage probability. The results showed a similar performance across all methods when PS overlap was strong. However, RF consistently outperformed the other methods when PS overlap was not strong and under non-additive and non-linear scenarios. The results suggest RF to be a more effective approach for PS estimation than the other proposed methods when applying IPTW analysis to complex survey data for population average treatment effects. The methods were applied to data from the Medicare Beneficiary Current Survey for years 2002–2019 to estimate the impact of hospice use on end-of-life healthcare costs. Findings from the real-world example show that hospice use was significantly associated with reduced end-of-life healthcare costs of Medicare Beneficiaries.