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An adverse drug reaction (ADR) is an unintended response induced by a drug. It is important to determine the association between drugs and ADRs. There are many methods to demonstrate this association. This systematic review aimed to examine the analysis tools by considering original articles that introduced statistical and machine learning methods for predicting ADRs in humans. A systematic literature review of EMBASE and PubMed was conducted based on articles published from January 2015 to March 2020. The keywords were statistical, machine learning, and deep learning methods for the detection of ADR signals in the title and abstract. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines. In total, 72 articles were included in the current systematic review; of these, 51 and 21 addressed statistical and machine learning methods, respectively. This study provides a graphical overview of data-driven methods for detecting ADRs with multiple data sources for patient drug safety.