Motivation: High-dimensional mediation analysis is critical for dissecting causal pathways in complex diseases. However, existing methods struggle to handle censored survival outcomes and unobserved confounders in epigenetic data. There is a pressing need for scalable, statistically robust frameworks to identify epigenetic mediators of exposure–outcome relationships, especially in cancer research where survival data are central and molecular pathways are highly complex and interconnected. Results: We introduce HDMAX2-surv, a two-step framework extending HDMAX2 to survival analysis. HDMAX2-surv integrates (i) latent factor modeling to adjust for unobserved confounders, (ii) flexible survival models (Aalen additive hazards and accelerated failure-time models). Simulations demonstrated superior performance over state-of-the-art methods (e.g., HIMA) in mediator selection and effect estimation. We integrated this approach with causal discovery frameworks and immune deconvolution algorithms to dissect multi-pathway mediation mechanisms. Applied to TCGA pancreatic adenocarcinoma data (n=112), HDMAX2-surv identified 36 aggregated methylated regions (AMRs) mediating tobacco exposure effects on survival, including immune-mediated pathways undetectable via gene expression alone. Availability and implementation: HDMAX2-surv is implemented in R and available on GitHub (https://github.com/bcm-uga/tims-pdac), with documentation and example scripts for reproducibility.