The Intelligent Image and Video Analysis System (IIVAS) automates the interpretation of visual data, allowing for efficient and precise processing of large-scale imagery and motion pictures. It aids in activities such as object detection, recognition of facial features, and video surveillance, hence improving decision-making processes, security, and real-time tracking in various industries. The architecture of IIVAS is scalable and flexible, allowing for the easy integration of various functionalities while maintaining effectiveness and performance. Deep learning models are used for the recognition and detection of objects to precisely recognize different items in pictures and videos and categorize them. In order to thoroughly understand the system, this paper introduced the performance of You Only Look Once (YOLO) model versions that are trained on huge datasets to guarantee excellent accuracy and robustness across many settings. Due to higher accuracy with least computational resources, future developments for YOLO models include improved medical imaging analysis, real-time object detection, collaboration with autonomous systems such as drones and self-driving cars, and use in smart cities for traffic surveillance, security, and customized AI-driven experiences.