This research paper explores the utilization of Principal Component Analysis (PCA) for the detection of video forgeries which encompasses the identification of duplicated frames, deleted frames, and inserted frames within the video content. PCA, a potent dimensionality reduction technique widely employed across various domains, is explored for its adaptability to video data. The approach involves preprocessing video frames, extracting principal components, and establishing a PCA-based feature space. The primary objective of this method is to identify irregularities and inconsistencies within video content, which may signify potential forgery. The proposed methodology is rigorously tested using a diverse dataset, with a focus on evaluating the performance of the PCA-based forgery detection system. The results show promising accuracy and precision, underscoring the potential effectiveness of PCA in video forgery detection. We have undertaken a comparative analysis with some of the state-of-the-art techniques. The findings from our observations demonstrate that the proposed method outperforms some established techniques in the detection of video forgery.