The friction stir spot welding (FSSW) process is a novel technique that overcomes the limitation of resistance spot welding. Recently, FSSW used for welding of polymers which are difficult to be joined by traditional welding processes. The demand for Acrylonitrile Butadiene Styrene (ABS) for industrial applications has increased in recent years. However, to employ this technique the challenge is to get optimal FSSW parameters setting to achieve the best weld strength during the welding of ABS sheets. To achieve this, in the present work, full factorial experimental design layout was employed to investigate the effect of process parameters on weld strength i.e., ultimate tensile strength (UTS) and percentage elongation during FSSW of ABS-ABS sheet in butt configuration. To predict the UTS and percentage elongation, machine learning regression namely, linear, polynomial, support vector machine, and decision tree was used. Further, the study includes the identification of the fracture patterns post tensile test specimens based on the topography of the fracture surface under scanning electron microscopy. It was found that plunge depth is the most significant parameter followed by spindle speed and dwell time. The optimal setting of process parameters i.e., spindle speed of 1000 rpm, plunge depth of 1 mm, and dwell time of 40 seconds resulted in maximum UTS of 7.849 MPa. The maximum value of percentage elongation obtained was 5 at the parameter setting of spindle speed of 1000 rpm, plunge depth of 0.8 mm, and dwell time of 40 seconds. Polynomial regression outperformed in the prediction of UTS and percentage elongation with an R-square of 0.99.