The rapid accumulation of digital data, coupled with advances in computational capacity, has ushered in a new era of data exploration and analysis. This shift presents significant opportunities for both accident analysis and broader social science research. Despite these developments, traditional social science disciplines—including accident analysis—have been slow to adopt powerful machine learning and data mining techniques. Moreover, they often conflate explanatory power with predictive power. This paper aims to (1) examine the theoretical foundations of machine learning and data mining within the context of the digital era, and (2) demonstrate the application of these methods to a GPS dataset and an insurance dataset for the purpose of enhancing accident prediction modeling.