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Abstract The rapid digitization of recruitment platforms has significantly increased the volume of job applications received by organizations worldwide. Human resource departments often face challenges in efficiently processing and evaluating thousands of resumes for a single job posting. Traditional manual resume screening methods are time-consuming, inconsistent, and susceptible to unconscious bias, which can lead to unfair candidate selection and inefficient recruitment processes. Moreover, conventional keyword-based filtering systems used in many recruitment platforms fail to capture contextual meaning, semantic relevance, and the overall suitability of candidates for specific job roles.This research proposes an intelligent Artificial Intelligence (AI)-driven framework for automated resume evaluation and precision job matching. The proposed system integrates advanced Natural Language Processing (NLP) techniques and machine learning algorithms to enhance the efficiency and accuracy of recruitment systems. The framework performs automated resume parsing, skill extraction, semantic analysis, and candidate ranking based on job descriptions and organizational requirements. By leveraging AI models, the system is capable of identifying relevant skills, educational qualifications, professional experiences, and contextual relationships between candidate profiles and job requirements.The architecture of the proposed framework consists of multiple stages including data pre-processing, feature extraction, semantic similarity computation, machine learning-based classification, and ranking optimization. NLP techniques such as tokenization, named entity recognition, and word embedding are utilized to analyse resume content and job descriptions. Machine learning algorithms are then applied to predict candidate suitability scores and generate ranked candidate lists for recruiters.Experimental evaluation of the framework demonstrates improved accuracy, efficiency, and fairness compared with traditional keyword-based recruitment systems. The proposed AI-driven model significantly reduces manual workload for recruiters while enhancing transparency and objectivity in the hiring process. Furthermore, the framework supports scalable recruitment operations suitable for large organizations and online recruitment platforms.The findings of this study highlight the potential of AI technologies in transforming modern recruitment systems by enabling intelligent resume analysis, reducing bias in candidate selection, and improving overall hiring quality. The proposed framework provides a practical solution for organizations seeking to adopt data-driven, automated, and ethical recruitment practices in the digital era.