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Plastic asphalt mixtures (PAMs) have garnered attention recently, but their field application has been limited due to a lack of understanding of asphalt mix behavior following modification. A modelling tool that can calculate the plastic influence on the characteristics of asphalt mixtures is required to close this gap. Hence, this study offers a performance analysis of various machine learning (ML) models in predicting the performance of PAMs through its various properties. These models include three methods, decision tree (DT) as an individual technique, adaboost regressor (AR), and bagging regressor (BR), as ensemble techniques for prediction of fundamental properties of PAMs i.e. air voids (Va), marshall flow (MF), marshall stability (MS), tensile strength ratio (TSR), and indirect tensile strength (ITS). A series of experimental works and their results on the PAMs properties were collected through literature, to …