Nature-inspired optimization (NIO) algorithms have gained quite a popularity among the researchers due to their good performance on difficult optimization problems. Recently, machine learning (ML) algorithms dealing with the generation of knowledge automatically from data have been often integrated into NIO algorithms to enhance their performance. One of the widely used popular NIO algorithms is an artificial bee colony (ABC) algorithm mimicking the intelligent foraging behaviour of real honeybees. In order to improve the performance of standard ABC, some hybridization studies of ABC and ML techniques have been performed to introduce more intelligent versions of ABC that can be used for solving the optimization problems arising in ML and other areas. This study presents a survey on the studies combining ABC with ML techniques for enhancing the performance of ABC algorithm and provides a discussion on how ML techniques have been adapted so far and can be employed for improving ABC further. We hope that this study would be very helpful for the researchers dealing with ML and NIO algorithms, particularly ABC.