The growing complexity and volume of recruitment tasks have rendered conventional resume screening methods inefficient, prone to bias, and susceptible to personal judgment. This study examines the application of advanced technologies, particularly large language models (LLMs) and generative AI, in automating resume screening processes. By leveraging state-of-the-art natural language processing (NLP) techniques, this system enhances the speed and accuracy of candidate evaluation. It addresses key issues such as bias and subjectivity, offering a fairer, more evidence-based approach to recruitment, particularly in organizations with significant recruitment turnover. Furthermore, this automated resume screening system promotes diversity by ensuring bias-free evaluations and mitigating the impact of human error in candidate selection. LLMs and machine learning algorithms not only streamline the hiring process but also enhance the ability to match candidates to the right roles based on skills and qualifications. This study underscores the importance of incorporating these advanced technologies to build a more equitable and efficient recruitment process in today’s data-driven employment landscape.