In the modern recruitment landscape, managing and evaluating large volumes of job applications efficiently remains a significant challenge for organizations. Traditional hiring processes often involve extensive manual screening, leading to inefficiencies and potential biases. To address these limitations, an advanced resume analysis system, FitFinder, has been developed. This system integrates a web-based interface built using Flask, enabling seamless multiform at resume uploads (PDF, DOCX, and DOC), coupled with a robust text extraction and cleaning pipeline. It applies role-specific keyword scoring to assess candidates and uses an ensemble machine-learning approach for ranking resumes on the basis of predefined criteria. A key feature of FitFinder is its comprehensive integration of resume processing, encompassing file uploads, text extraction, data cleaning, and automated candidate ranking. Furthermore, it upgrades performance graphs to improve judgment. The suggested approach enhances the effectiveness and precision of the hiring process by reducing manual labor and offering transparent, data-driven assessment. In the end, this paradigm facilitates more informed hiring choices, which enhances workforce quality and streamlines talent acquisition.