Due to high demand for energy, oil and gas companies started to drill wells in remote environments conducting unconventional operations. In order to maintain safe, fast, and more cost-effective operations, utilizing machine learning (ML) technologies has become a must. The harsh environments of drilling sites and the transmission setups are negatively affecting the drilling data, leading to less than acceptable ML results. For that reason, a big portion of ML development projects was actually spent on improving the data by data-quality experts. The objective of this paper is to evaluate the effectiveness of ML on improving the real-time drilling-data quality and compare it to human expert knowledge. To achieve that, two large real-time drilling datasets were used; one dataset was used to train three different ML techniques: artificial neural network (ANN), support vector machine (SVM), and decision tree (DT); the second dataset was used to evaluate it. The ML results were compared with the results of a real-time drilling-data-quality expert. Despite the complexity of ANN and good results in general, it achieved a relative root-mean-square error (RRMSE) of 2.83%, which was lower than DT and SVM technologies that achieved RRMSE of 0.35% and 0.48%, respectively. The uniqueness of this work is in developing ML that simulates the improvement of drilling-data quality by an expert. This research provides a guide for improving the quality of real-time drilling data.