The increasing utilization of multimedia content in education, marketing, and digital communication has created a growing demand for efficient video generation systems. This paper presents an Automated Video Generator that converts textual input into narrated videos using a modular deep learning-based pipeline. The proposed system generates a narrative, retrieves context-relevant images, synthesizes speech using text-to-speech technology, and composes synchronized videos through a web-based Django framework. Experimental results demonstrate improved efficiency, stable performance, and audio-visual synchronization, making the system suitable for educators, marketers, and content creators while reducing manual effort and production time. ``` Batch11 Automated Video Generator Using Deep Learning.pdf