This project focuses on developing a deep learning-based neural network architecture for automatic timestamp generation in video content, leveraging both audio and visual inputs. The system aims to accurately detect and mark relevant video segments by analyzing synchronized audio-visual data, such as speech and corresponding facial movements. The input comprises audio features like Spectral Feature Extraction Based on Mel-scale Frequencies and visual features such as pixel data or facial landmarks, which are processed through a series of fully connected hidden layers to learn complex temporal relationships. The neural network predicts timestamps for significant video events, offering precise synchronization of video frames with audio signals. To enhance performance, advanced models like Long Short-Term Memory (LSTM) networks are integrated, addressing temporal dependencies across video sequences. The primary applications of this system include automatic subtitle generation, dialogue extraction, and lip-sync accuracy for multimedia content. This approach aims to improve efficiency and accuracy in video processing tasks, particularly in areas that require precise temporal alignment between audio and visual elements. In the future, this model can be further optimized for real-time video applications and extended to support multi-modal video processing in various languages and environments.