Nowadays, many job ads are attracting a vast number of applications in less time on the internet. Screening and sorting these resumes manually is a very time-consuming process and costs a huge amount of money for recruiting organizations. This research paper suggests an automated method for matching resumes to open vacancies. To identify the most accurate machine learning approach, it investigates several of them, including Random Forest, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree, Logistic Regression, Naïve Baye’s Classifier. Based on the findings of this research, Random Forest produces more accurate results for a given dataset. By using this automatic resume categorization method, the recruiting process will be revolutionized in terms of cost, time, and justice.