Data is the new oil, and in today’s data-driven world, the ability to generate, store, retrieve, and derive meaningful insights from it is crucial. This data often includes unstructured multimedia content, which poses unique challenges for effective management and utilization. Videos are crucial as they contain data such as object audio motion text. Content-based video retrieval (CBVR) is critical for managing and searching large multimedia databases, as it enables retrieval based on the actual content of videos rather than metadata or textual descriptions. Traditional retrieval methods often fail to achieve accuracy due to limited content representation capabilities. In response, the paper proposes a CBVR system that utilizes a deep learning based video representation and a classification approach utilizing a pre-trained ResNet-152V2 model to learn feature representation and classification scores. Experiments were conducted on the DGX-1 server using the UCF-101 Human Action Recognition Dataset, which comprises 13,300 videos across 101 categories. The proposed method outperformed the most competitive existing approaches, achieving a 30% higher precision and recall, a 20% improvement in F1-score, and a 40% increase in AUC, demonstrating its superior effectiveness in CBVR.