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A 5.3% increase in motor vehicle traffic crashes in 2012 [1] brings up the discussion of related traffic safety parameters. This paper considers the analytical assessment and evaluation of various highway safety factors that will eventually trigger fatalities. The related safety parameters are mainly divided into four categories--economical investment, system usage, road condition, and personal safety. Three data mining algorithms--K-nearest Neighbors algorithm (KNN), Random Forest and Support Vector Machine (SVM), and also a probabilistic Artificial Neural Network (ANN)--are used for the prediction of highway fatalities among the eight different safety indicators. According to the Bureau of Transportation Statistics’ most recent available data, the analysis of this study covers the years from 2003 to 2011. The preliminary results indicated that out of the three, the proposed Random Forest data mining approach predicted the data with the highest percentage. The sensitivity analysis is conducted and, according to the results, the bad-road condition is found to be the most important factor that affects the highway fatalities. This research shows the significant relationship between safety indicators and highway fatalities, and also provides guidance for policy makers when it comes to preventing highway fatalities in United States.