A notable trend in the rapidly evolving mobile technology domain is the increasing reliance on encrypted network packets to strengthen privacy and security. Nevertheless, certain unencrypted elements, such as packet size and other critical Internet functionalities, remain exposed despite this encryption. This project is dedicated to harnessing these aspects by developing a tool adept at classifying encrypted network packets. Utilising Deep Learning models, the tool is designed to categorise the traffic on a network and deduce the types of applications on mobile phones. Positioned at the intersection of network security and AI-driven analysis, this innovative project emphasises developing and refining cutting-edge deep-learning models. These models are specifically designed to pinpoint the applications generating network traffic despite encryption. This capability is made possible by identifying unique patterns and traits within the data flow indicative of specific applications installed on a smartphone. This project is committed to privacy, and ethical considerations are a vital aspect of this project. While encrypted packets bolster security, the ability of tools to categorise their contents and deduce installed applications poses privacy challenges. The project addresses these concerns by offering a solution that respects user privacy while yielding valuable insights into network traffic. Therefore, the project approaches these challenges with a solution that respects user privacy while providing valuable insights into network traffic. This balance of privacy with technological advancement sets a new standard in network traffic analysis, underscoring the project's innovative and conscientious approach.