Random Forest is an ensemble machine learning method developed by Leo Breiman in 2001. Since then, it has been considered the state-of-the-art solution in machine learning applications. Compared to the other ensemble methods, random forests exhibit superior predictive performance. However, empirical and statistical studies prove that the random forest algorithm generates unnecessarily large number of base decision trees. This may cost high computational efficiency, predictive time, and occasional decrease in effectiveness. In this paper, Authors survey existing random forest pruning techniques and compare the performance between them. The research revolves around both the static and dynamic pruning technique and analyses the scope of improving the performance of random forest by techniques including generating diverse and accurate decision trees, selecting high performance subset of decision trees, genetic algorithms and other state of art methods, among others.