Resume screening is a critical step in the recruitment process, traditionally relying on manual review to assess candidates' qualifications. The advent of Natural Language Processing (NLP) has introduced advanced techniques to enhance this process by automating and optimizing resume evaluation. This paper explores the application of NLP in resume screening, focusing on methods such as keyword extraction, semantic analysis, and machine learning models. It discusses how NLP algorithms can identify relevant skills, experiences, and qualifications by analyzing the textual content of resumes. Furthermore, the integrating of NLP with applicant tracking systems (ATS) offers improved efficiency and accuracy in matching candidates to job requirements. The paper also examines challenges such as handling diverse resume formats and ensuring fairness in automated evaluations. By leveraging NLP, organizations can achieve a more streamlined and objective screening process, ultimately leading to better hiring outcomes and reduced bias in recruitment.