In the project, the aim is to create an automated system to help with resume screening. Typically, resumes do not come in a standard format. They vary in structure, writing style, and presentation. Therefore, processing resumes is far from easy compared to other texts. Unlike data in a neat and organized structure, as in academic data sets, processing a resume is a challenge. In academic data sets, the data is clean and in a neat organization.\nIn order to manage these problems, certain textual processing techniques were applied, and the steps included normalization, tokenization, and feature selection. The system was tested numerous times in order to improve its ability to execute perfectly even with imperfect data. Over time, the scope extended beyond how to improve the precision of the program to include the influence of data quality in the real world. Aside from that, there are also ethical considerations involved in the making of the system, such as the implication that biases in the data might be inadvertently reflected in automated systems, where measures are being taken to make a more transparent and interpretable system, keeping recruiters involved by introducing flexible decision thresholds to keep human judgment in charge. Overall, however, it is a system meant to assist, not replace, the recruiter. In this manner, it can help speed up the hiring process and make it more efficient by reducing human error and quickly identifying potential candidates.