Social networks serve as dependable platforms for valuable communication channels and the dissemination of information on a global scale, with millions of users utilizing them on a daily basis. While they have emerged as a means of disseminating information, they have also quickly transformed into a conduit for spreading false information, rumors, unsolicited messages, promotional content, fabricated news, and other undesirable content. Both spammers and non-spammers are present on social networks, with spammers typically sending advertisements and unwanted messages to users. Unfortunately, many users unknowingly spread unwanted messages, contributing to the problem. To address this issue, a model has been developed to recognize spammers on social networks. This process is based on the user's actions and patterns, as well as information-based features such as URLs, posts, replies, mentions, tweets, and retweets. The Recursive Feature Elimination (RFE) method is used in conjunction with five classifiers, including Random Forest, Logistic Regression, SVM, XGB, and Adaptive Boosting. Data is collected from social networks and implemented using the Sci-Kit Learn library in Python. The model's performance is evaluated using metrics such as TP and FP rate, precision, and accuracy.