In the current era, with the advent of the information age, the need for obtaining large amounts of data quickly and efficiently is ever-increasing. One of the possible solutions to this problem is an improved data structure, Bloom Filter. A Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set or not. It is based on hashing and is typically used to add and look up elements in a set. Bloom filters are useful when the data is large, and memory is limited. There are many applications of bloom filters, such as searching items, recommendations, weak password detection, and safe browsing. In this talk, we will explore the Bloom filter and its applications.