This review explores the transformative role of AI within Applicant Tracking Systems (ATS), examining how AI-driven resume parsing contrasts with traditional methods to streamline recruitment efforts. It analyses the technical foundation of contemporary ATS, emphasizing how NLP and machine learning contribute to information extraction and candidate ranking processes. The review addresses ethical concerns, including algorithmic bias and transparency shortfalls, and evaluates performance metrics like accuracy and efficiency gains. Emerging trends such as multimodal data integration are also considered. We discuss how to ensure fairness in AI-driven hiring, proposing actionable solutions for balancing automation with ethical oversight.