Machining and achieving good surface finish in Ti-6Al-4V is a challenging task. Several factors influence surface finish of which some can be controlled and are mainly the machining parameters and some are considered uncontrolled and happens to be the output of the machining process like tool wear and cutting tool vibrations. Several researchers have made attempts to identify these parameters and study both experimentally and through modeling. This study deals with the experimental correlation between machining parameters namely cutting speed, feed rate, depth of cut and uncontrollable parameters namely tool flank wear and cutting tool vibrations on two surface roughness parameters Ra and Rt, while turning Ti-6Al-4V using coated carbide inserts. Further, in order to study the effect of individual parameters statistically on the responses, a widely used statistical technique namely Response Surface Methodology (RSM) and a new technique, Random Forest Regression (RFR) have been applied to these experimental data to model and predict the values of surface roughness parameters. Results revealed that RFR performed better than RSM. It has been found that tool wear is the most dominant parameter affecting surface roughness parameters followed by feed rate and cutting tool vibrations have a direct correlation with surface roughness parameters.