The artificial intelligence field’s textual description technology is used to create high-quality, temporally consistent videos. Despite issues with motion consistency, semantic alignment, and computational efficiency, this technology has enormous potential for use in virtual reality, entertainment, and education. Some recent approaches use pre-trained text-to-video models for direct video synthesis, while others use diffusion models with spatiotemporal blocks for smooth transitions. This study proposed a hybrid framework technology that implement diffusion models and GANs for improving the results. It is used by temporal attention processes, gradual training, and optimization strategies. These improvements in computer economy, video quality, and motion coherence enable long and dynamic sequences. Authenticity verification and bias prevention are two ethical challenges that are part of responsible development. By providing a comprehensive overview of advancements and challenges, this study encourages further innovations in the field.