Abstract
Instant messaging has become a cornerstone of modern communication, playing a significant role not just in daily interactions but also in areas like digital forensics. This paper introduces a Chat History Analysis Module designed to simplify and enhance the analysis of chat datasets from popular messaging platforms. By leveraging advanced machine learning techniques and an efficient vector database, the module can store, search, and retrieve chat messages based on content, sender, receiver, and timestamp.The module was tested using a synthetic dataset of 330 messages, each enriched with metadata such as sender and receiver information, timestamps, and message statuses. It supports multiple languages and generates visualizations that reveal communication patterns, trends over time, app usage preferences, and key relationships between senders and receivers.This solution addresses challenges like handling large volumes of data and multilingual content, bridging the gap between raw chat data and actionable insights. It is a valuable tool for practitioners in the field of digital forensics and aligns with the themes of modeling, simulation, and intelligent computing