Interface traps are of particular concern for highly scaled-down semiconductor devices. They cause trapping and de-trapping of charge carriers and have an adverse effect on device characteristics and variability. Therefore, in this work, the influence of randomly generated interface traps (RITs) on device characteristics of 16-nm-gate high-κ/metal gate bulk fin field-effect transistors (FinFETs) is investigated for experimentally validated simulated data. A machine learning (ML) model is proposed here to imitate the device simulation results. The impact of variation of these multi-point defects is analyzed by generating RITs at the interface of gate-oxide and silicon channel of the explored bulk FinFETs. The statistical fluctuations induced by RITs are analyzed by predicting the variations in threshold voltage (VTH), subthreshold slope (SS), drain-induced barrier lowering (DIBL), off-state current (IOFF), and transconductance (gm) using the proposed ML model with high accuracy and small error, in much less computational cost. This work shows the possibility of accelerating the random defects analysis using the technique of machine learning.