Machine learning operates at the intersection of statistics and computer science, employs algorithms to scrutinize data and derive valuable insights, contributing to the decision-making process by extracting meaningful information. With the widespread adoption of big data in various applications, deriving valuable insights from this real-world data poses several challenges in extracting meaningful information. Factors like time and memory complexities add to the difficulties in this task. Despite significant progress and sustained interest in machine learning algorithms, there is a surprising lack of review research that consolidates, analyzes, and compares the computational complexity of these models. This paper aims to examine and contrast the computational complexities of various commonly used machine learning methods, with the goal of understanding and optimizing their performance for large datasets.