The challenge of extracting useful insights from unstructured data in the presence of big digital information is still present today. AI Intensive Timestamp based Summarization proposes a novel framework to automatically extract and summarize pivotal events, along with their corresponding timestamps from different data sets. Making use of Natural Language Processing and deep learning machine learning approaches, the system checks text data for temporal markers, extracts salient events and produces verbose summaries. The method proposed shall make the historical analysis easier and trend detection along with automated reporting of large dataset summaries more concise. Technique is implemented using Named Entity Recognition for date extraction and transformer models in case of summarization Experiments show that AI-based timestamp summarization is efficient in enhancing IR results and autodoc reliability. Making This Research Unique and Contributing to the much Expanding AI-driven text analysis filed, a Scalable in nature for timestamp extraction on domain wise basis.