As per with the development of social media and modern technologies, advertising new job openings has recently become a very prevalent problem in the current world. Therefore, everyone will have a lot of reason to be concerned about bogus job postings. Fake job posing prediction presents a variety of difficulties, just like many other categorization tasks. In order to determine whether a job posting is legitimate or fake, this study advocated using a variety of data mining techniques and classification algorithms, including KNN, decision trees, support vector machines, naive bayes classifiers, random forest classifiers, and deep neural networks. The For this deep neural network classifier, three thick layers were used. A bogus job advertisement can be predicted with a classification accuracy of about 98% by the trained classifier using DNN. The article suggests an automated application that uses machine learning-based classification approaches to prevent fraudulent job postings online. The outputs of various classifiers are evaluated in order to determine the best employment scam detection model. These classifiers are used to verify fraudulent posts on the web. It assists in identifying phoney job postings among a large number of postings. For the purpose of identifying fake job postings, two main categories of classifiers—single classifiers and ensemble classifiers—are taken into consideration. Nevertheless, experimental findings show that ensemble classifiers are the most effective classification to identify fraud over the single classifiers