The volume of applications for jobs in modern recruitment inherently calls for intelligent systems that could automate the filtering of resumes. This work proposes the integration of Natural Language Processing with a Decision Support System (DSS) to develop an enhanced recruitment pipeline for automation in the filtering of resumes against predefined criteria. Accordingly, it provides the system with the ability to extract information from a lot of unstructured data, usually present in a resume, very effectively. It analyses this thoroughly and classifies it meticulously, using advanced NLP techniques, including but not limited to Named-entity Recognition (NER) and semantic analysis. By leveraging and using such sophisticated techniques, the system not only increases the recruitment process by manifold times for speed but also enhances the overall efficiency in handling applications. This approach further helps in substantial reduction of the risk of human biases that may inadvertently influence the candidate selection process. The architecture of the proposed system is explained, the models and algorithms of NLP employed are described, and the performance of the system is evaluated using relevant metrics. The results presented attest that the integration of NLP with DSS can significantly increase the accuracy and efficiency in resume filtering, thereby offering a promising solution to modern-day challenges related to recruitment.