The Keyword-Based Video Retrieval System (KB-VRS) is a potential solution to organize and access vast amounts of video content. We have focused our research efforts on building a reliable and efficient KBVRS and this paper presents an in-depth review of our findings. Our solution leverages advanced technologies like Natural Language Processing (NLP) and Optical Character Recognition (OCR) to automate the analysis and indexing of video content based on user-defined keywords. The KBVRS extracts keywords and key phrases from video frames and audio transcripts, enabling efficient searching and retrieval of relevant video information, thus enhancing multimedia content management. Our goal is to overcome the limitations of manual tagging and classification by providing scalable and customized KBVRS for multimedia content organization and retrieval. The system caters to users, including educators, researchers, media professionals, and content creators. The user-friendly interface and intuitive search feature facilitate easy access and utilization of multimedia information. We have shown through rigorous experiments that our system is resilient and effective in retrieving relevant video content based on user queries. Our paper contributes to keyword-based video retrieval systems by laying the groundwork for future research in this rapidly evolving field. These insights pave the way for further exploration in this dynamic field. Our study empowers better decision-making processes in video content management.