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Context Code smells in the software systems are indications that usually correspond to deeper problems that can negatively influence software quality characteristics. This review is a part of a R&D project aiming to improve the existing codebeat platform that help developers to avoid code smells and deliver quality code. Objective This study aims to identify and investigate the current state of the art with respect to: (1) predictors used in prediction models to detect code smells, (2) machine learning/artificial intelligence (ML/AI) methods used in prediction models to detect code smells, (3) code smells analyzed in scientific literature. Our secondary objectives were to identify (4) data sets and projects used in research papers to predict code smells, (5) performance measures used to assess prediction models and (6) improvement ideas with regard to code smell detection using ML/AI. Method We conducted a …