Web browsers have become an essential part of our lives acting as a gateway to immense information available on the web. Tabbed interfaces, that are ubiquitous to nearly all modern web browsers, allow for segmentation, facilitating efficient navigation and multi-tasking. Constrained resources of mobile devices provide limited spaces where cluttering can lead to loss of context & defeat the very purpose of browsing. However, as browsing scales up, duplicate tabs can quickly become overwhelmingly large in numbers, introducing performance issues and hindrance in user experience. Previous researches in solving this problem have relied on Uniform Resource Locator (URL), web page internal structure and style similarity to discern similarity. Apart from this, web extensions also act as a helping tool in tab management. This paper leverages statistical methodologies of Term frequency-Inverse Document frequency (TF-IDF) and cosine similarity to compute a meaningful metric between web pages and expedite duplicate tab detection, further extending this solution’s reach to include Artificial Intelligence (AI).