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The recruitment process has become increasingly challenging due to the large volume of resumes received for every job opening. Manual screening of resumes is time-consuming, prone to human errors, and may result in overlooking qualified candidates. Similarly, students often face difficulties in tailoring their resumes according to specific job requirements, reducing their chances of being shortlisted by Applicant Tracking Systems (ATS). To address these challenges, this project roposes an AI Resume Screener and Student Resume Optimizer that automates resume screening, candidate ranking, and resume enhancement. The system extracts information from PDF and DOCX resumes using multiple text extraction techniques, including pdfplumber, pypdf, pdfminer, and Optical Character Recognition (OCR) using Tesseract for scanned documents. It identifies candidate skills, educational qualifications, and contact information, and compares them with job requirements to calculate a match score. The proposed system provides separate functionalities for HR users and students. HR users can upload job descriptions and multiple resumes to obtain ranked candidate lists based on a scoring algorithm that evaluates skills, education, and contact information. Students can upload their resumes and receive AI-powered optimization suggestions generated using OpenAI GPT-4o-mini, resulting in ATS friendly resumes while preserving the original formatting. The system uses SQLite for data storage and supports efficient management of users, screening records, and resume submissions. By automating resume analysis and optimization, the proposed system reduces recruitment effort, improves candidate selection accuracy, and enhances students’ employability by helping them create job-specific professional resumes