My research interest is optimizing parallel discrete event simulation (PDES) with machine learning approach. I concentrate on applying machine learning for predicting models that accelerate simulations, addressing memory management challenges, and minimizing rollbacks features not present in conventional Time-Warp methods. This research aims to boost simulation efficiency and reliability, contributing to advancements in the field.