Recent years have witnessed an increased interest in online education, both massive open online courses (MOOCs) and small private online courses (SPOCs). This significant interest in online education has raised many challenges related to student engagement, performance, and retention assessments. With the increased demands and challenges in online education, several researchers have investigated ways to predict student outcomes, such as performance and dropout in online courses. This paper presents a comprehensive review of state-of-the-art studies that examine online learners’ data to predict their outcomes using machine and deep learning techniques. The contribution of this study is to identify and categorize the features of online courses used for learners’ outcome prediction, determine the prediction outputs, determine the strategies and feature extraction methodologies used to predict the outcomes, describe the metrics used for evaluation, provide a taxonomy to analyze related studies, and provide a summary of the challenges and limitations in the field.