Developing spam detection algorithms for Bengali language emails presents a unique set of challenges. With the increasing use of the Bengali language in digital communication, users are facing a growing volume of unwanted emails, which not only clutter their inboxes but can also pose security risks. The main issue is that most existing spam detection technologies are primarily designed for English or other widely used languages, leaving Bengali language users with less effective tools to filter out spam. The challenge involves creating an algorithm that understands the nuances of the Bengali language, including its vocabulary, grammar, and common colloquialisms, to accurately identify and filter spam emails. This requires a deep learning approach that can adapt to the constantly evolving tactics of spammers, such as using subtle language changes or disguising spam as legitimate emails. Additionally, the solution must be efficient and fast, ensuring that it can process a large volume of emails without significantly delaying delivery. It also needs to minimize false positives, where legitimate emails are mistakenly marked as spam, and false negatives, where spam emails bypass the filter. In summary, the task is to develop a sophisticated spam detection algorithm specifically for Bengali language emails that is adaptable, accurate, and efficient. This technological solution would significantly enhance the email experience for Bengali-speaking users by reducing unwanted content and increasing online security.