There have been many sorting algorithms available, but it’s always been a challenge to find out which one is the right to be implemented to solve real-world challenges when the dataset is huge to give you the best and most optimal results. The objective of this paper is to find from among the three candidate algorithms – Quicksort, Insertion sort, and Counting sort, to find out which one outperforms the other dynamically by the use of a Machine Learning algorithm on real-time data set with varied volume and variations to help implement the algorithm which gives most optimal results.