Interfacial mechanical properties of adhesive joints are very crucial in board applications, including composites, multilayer structures, and biomedical devices. Establishing traction-separation (T-S) relations for interfacial adhesion can evaluate mechanical and structural reliability, robustness, and failure criteria. Due to the short range of interfacial adhesion such as micro to nanoscale, accurate measurements of T-S relations remain challenging. The advent of machine learning (ML) became a promising tool to predict materials behaviors and establish data-driven mechanical models. In this study, we integrated a state-of-the-art ML method, finite element analysis (FEA), and standard experiments to develop data-driven models for characterizing the interfacial mechanical properties precisely. Macroscale force-displacement curves are derived from FEA with incorporation of double cantilever beam tests to generate the dataset for ML model. The eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are used to determine T-S relations with R2 score of 98.8% and locate imperfections at the interface with accuracy of around 80.8%. The outcome of the XGBoost models demonstrated accurate predictions and fast calculation speed, outperforming several other ML methods. Using 3D printed double cantilever beam specimens, the performance of the ML models is validated experimentally for different materials. Furthermore, a XGBoost model-based package is designed to obtain different adhesive materials T-S relations without creating a database or training a model.