Video processing has been transformed by the quick development of artificial intelligence (AI), opening up an unlimited number of creative uses across multiple industries. An AI-based video processing system is thoroughly examined in this research, with an emphasis on its design, algorithms, and possible applications. The suggested system makes use of cutting-edge deep learning methods for tasks including video enhancement, object detection, scene comprehension, and action recognition. The system provides great accuracy and efficiency in real-time performance by utilizing recurrent neural networks (RNNs) and convolutional neural networks (CNNs). Additionally, the integration of edge computing capabilities enhances scalability and reduces latency in resource-constrained environments. This study also addresses key challenges, including data privacy, computational overhead, and the ethical standard implications of artificial intelligence in video processing. Future directions are discussed, focusing on hybrid AI models and the potential for federated learning to further optimize performance and privacy.