The rapid development of AI video generation has proliferated AI-generated videos through almost every segment of entertainment, education, and marketing. Despite such huge potential, this segment always stands out facing inconsistent video quality as one of the most critical challenges. It therefore follows that it sets up an integrated model with which to assess the quality of AI-generated videos in terms of fidelity, realism, consistency, accuracy, relevance, creativity, seamlessness, coherence, engagement, innovation, resolution and detail, color accuracy, frame rate and smoothness, AI artifacts and limitations, the uncanny valley effect, bias, and continuity. The study has critically analyzed the videos obtained from leading AI platforms to pinpoint key quality indicators and develop a robust benchmarking model capable of adapting to the changing standards of AI technology. A few of the key strengths seen for current AI tools (realism and resolution) were also clear weaknesses found (handling AI artifacts, continuity). This model offers an organized system that can be utilized to assess AI-generated videos (while identifying areas of enhancement) and also gives specific recommendations for moving forward with machine learning video generation. The research helps to create new benchmarks for AI powered media, drives innovation and improves the standard of AI driven video outputs in line with a rapidly expanding market demand for superior digital content.