Band, Shahab S.Ardabili, SinaSookhak, MehdiChronopoulos, Anthony TheodoreElnaffar, SaidMoslehpour, MassoudCsaba, MakoTorok, BernatPai, Hao-TingMosavi, Amir2022-07-142022-07-14© 20132022Band, S. S., Ardabili, S., Sookhak, M., Chronopoulos, A. T., Elnaffar, S., Moslehpour, M., . . . Mosavi, A. (2022). When smart cities get smarter via machine learning: An in-depth literature review. IEEE Access, 10, 60985-61015. doi:10.1109/ACCESS.2022.318171821693536https://doi.org/10.1109/ACCESS.2022.3181718http://hdl.handle.net/20.500.12519/684The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate. © 2013 IEEE.en-USCreative Commons Attribution 4.0 International License (CC BY 4.0)artificial intelligencebig datadata sciencedeep learningensemblemachine learningSmart citysmart gridReview