Since their creation, machine learning and artificial intelligence have become a part of our daily lives. A branch of artificial intelligence known as “machine learning” is a flexible concept with applications in nearly every field, including agriculture, industry, and medicine. One significant use of machine learning is targeted advertising, a strategy nearly all businesses use to increase sales. Advertising is essential for a company's brand value, and the current trend shows that almost everything is done online, where tailored advertising comes into play. Targeted advertising is highly profitable, with companies like Google and Facebook generating significant revenue by targeting users and promoting their clients’ products. Users are served advertisements based on their past ac tivities and interests, increasing the likelihood and success rate of purchases. Machine learning facilitates this by creating models that calculate the probability of a customer clicking on an advertisement. Decision trees and logistic regression are two effective methods for predicting this probability accurately. In this paper, we highlight how these methods identify the correct target audience and determine how likely they are to engage with the advertised product.