There is no doubt that the use of drugs has significant consequences for society, it introduces risk into the human life and causing earlier mortality and morbidity. Being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, artificial intelligence and machine learning systems can provide useful tools for raising the prediction rate of drug users. Recently, the psychologists approved the recent personality traits Five Factor Model (FFM) for understanding human individual differences. The aim of this work is to propose a rough sets theory based method to investigate the relationship between drug user/non-user (monthbased user definition) and the personality traits. The data of five factor personality profiles, impulsivity, sensation-seeking and biographical information of users of 21 different types of legal and illegal drugs are used to fetch all reducts and finally a set of classification rules are created to predict the drug user/nonuser(month-based user definition). The outcomes demonstrate the novelty of the current work which can be summarized as The set of generalized classification rules which pronounced with logic functions build a knowledge base with excellent accuracy to analyze drug misuse successfully and may be worthy in many applications.