Resume parsing is a crucial process for recruiters to identify suitable candidates for job vacancies. However, manual resume screening can be a time-consuming and tedious task, especially when dealing with a large volume of resumes. The use of machine learning techniques in resume parsing can automate this process and help recruiters quickly identify relevant candidates. This paper presents an abstract of a review of different approaches for resume parsing using machine learning techniques. We discuss the challenges of resume parsing, the preprocessing steps involved, the feature extraction techniques used, and the evaluation of different machine learning algorithms used for resume parsing. The “genism” library may summarize resumes for this system to use, and once classification has been completed as per the job description, prospective candidates may be rated using clustering methods to determine which resumes are most similar to the specified job description.