This paper demonstrates the contribution of machine learning algorithms in real-life problem resolutions, taking the example of fraudulent job postings detection. Therefore, the Support Vector Machine (SVM) is associated with a dataset that includes both real job postings and those considered to be fraudulent. Before using this dataset, the content was submitted to preprocessing techniques. Moreover, data mining techniques were relevant to extract essential features from the dataset. The disparity between fraudulent and non-fraudulent classes is addressed by applying the SVM-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) method, considering that synthetic samples from the minority class (fraudulent) are generated to improve the model performance. Also, the model was trained and tuned on processed data to achieve high performance. Additionally, the performance of the model was examined following key factors including accuracy, precision, recall, and F1-score, respectively. The correct or incorrect predictions obtained from the model are exposed by using the confusion matrix.