Recruitment procedures have been completely transformed by the quick development of artificial intelligence (AI) and natural language processing (NLP), with automated resume parsing emerging as a crucial talent acquisition tool. In order to speed up the candidate screening process, resume parsing entails extracting, organizing, and evaluating information from resumes. A thorough examination of several resume parsing strategies is given in this review study, including rule-based strategies, machine learning models, and deep learning-based strategies like Named Entity Recognition (NER) and Transformers. We also assess well-known resume parsing tools according on their accuracy, methods, and usefulness. The study also addresses important issues like inconsistent data, multilingual parsing, and moral dilemmas with AI-driven hiring. We conclude by discussing potential avenues for future research, highlighting the necessity of increased precision, bias reduction, and greater Applicant Tracking System (ATS) integration. Researchers and developers looking to improve resume parsing technology for more impartial and effective recruiting procedures might use this review as a starting point.