Outages that occur in power distribution systems can seriously endanger system operation in different ways. In recent years, with the explosion in data gathering within the smart grid framework, data analytics has emerged as a desirable tool in helping maintain power system security. In particular, with the deployment of an enormous number of intelligent electronic devices and various sensors, the data necessary for studying different outages have becoming available. Analyzing such data by employing analytical methods could shed light on understanding the characteristics of outages and could lead to developing models for analyzing, identifying, and predicting different outages.In this dissertation, the focus is on developing various approaches for analyzing, identifying, and predicting outages in power systems by using rigorous statistical methodologies, data analytics, and machine learning algorithms. Developing such approaches, however, requires addressing various practical challenges. As a result, those challenges are discussed throughout the dissertation and workable solutions are provided.The proposed approaches have the potential to provide not only a succinct view of the current system status but also more meaningful knowledge such as outage risks, and locations of potential problems, as well as suggestions on remedial actions. As such, this dissertation envisions a transformative framework that will demonstrate the use of innovative data analytics technologies for early warning of degrading operating conditions that may imperil system operation and/or quality.