The use of drugs and the effects of drug use is a serious problem in the United States. Recently the USA has experienced an increase in the use of opioids such as fentanyl which is one of the most common drugs involved in overdose deaths. In 2017, 59% of opium related deaths involved fentanyl [8]. The rise in drug abuse rates causes concern to homeland security professionals such as Transportation Security Officers as well as Customer Border Protection Officers, to be more attentive on how to implement methods for drug prevention. Air travel is one of the methods used to transport drugs to and from illegal suppliers and given the rise in deaths from opioid drug use and the harm to society that it can cause it is critical that new methods be identified to minimize drug trafficking. One method would be to decrease the transport of illicit drugs arriving in the country and transported around the country. In this research, we used data science techniques to create a robust tool that can enhance airport security and help prevent smuggling by detecting airport smugglers. The results of our research will be helpful to homeland security and customs and border patrol to effectively keep the public safe. The tool will be able to detect drug smugglers based on behavioral characteristics as personal characteristics gathered from statistics of apprehended drug smugglers. Specifically, we will use artificial intelligence and machine learning techniques to create a tool which can detect drug smugglers with a high degree of accuracy.