IoT sensors are extensively used in a variety of medical applications such as patient monitoring. In recent years, multiple papers have been published on the measurement of blood alcohol level (BAL) using more specialized biosensors. Transdermal alcohol content (TAC), a practical and non-invasive tool for measuring BAL, was employed in several recent research works. As BAL affects the person's way of walking, accelerometers (ACC) combined with TAC data fed to machine learning algorithms (MLA) were applied to predict the state, drunk or sober, of a person. However, the accuracy of prediction was not high enough to be considered reliable. In this paper, using an archived “BAR CRAWL” dataset, we implemented five MLA: Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), and Ada Boost (AB), with a variety of features to accurately predict the state of a person based on his alcohol levels. Furthermore, we defined various alcohol thresholds to predict the level of intoxication. Also, we were able to identify the intoxicated person. Our experimental results showed that we can achieve up to 28.9 % higher prediction accuracy with less processing time (PT) when compared to previous published works using the same dataset.