This chapter delves into the advanced application of Artificial Intelligence (AI) for video quality assessment and enhancement, crucial for improving real-time video streaming experiences. Techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and reinforcement learning are explored in depth, showcasing how these AI methods contribute to dynamic resolution adjustment, noise reduction, and adaptive bitrate streaming. The chapter highlights CNN-based approaches to video quality assessment, focusing on multi-scale analysis for detecting subtle quality degradations in real time. Additionally, GANs are discussed for their role in video upscaling, artifact removal, and noise reduction, providing insights into how AI enhances low-quality video into high-definition formats. Reinforcement learning, applied in adaptive bitrate streaming, is examined for its ability to optimize video delivery under fluctuating network conditions. The chapter is supported by real-world case studies demonstrating the tangible benefits of these AI-driven techniques in improving user satisfaction, reducing latency, and enhancing service efficiency. By integrating AI across various stages of video streaming, platforms can deliver consistently high-quality video, ensuring seamless user experiences even in challenging network environments.