Small wireless IoT smart sensors are becoming increasingly common in both domestic and industrial applications. They can store, processing, and transmitting large amounts of data, making them a valuable tool for decision-making. In this article, a new application for wireless IoT smart sensors is presented: the estimation of load and energy consumption in electric motors. This information is essential for the correct sizing of the motors. To perform the estimates, the smart sensors associate the use of Machine Learning techniques with electric motor modeling. These techniques allow that predictions of load and energy consumption are made with an adequate level of accuracy when compared to traditional techniques, such as dynamometer bench and energy analyzers. To validate the accuracy of the proposed technique, the 6 Sigma methodology was used to compare the energy consumption estimates by the IoT sensor with consumption readings performed by reference equipment as a commercial energy analyzer. The results of the comparison showed that IoT smart sensors are capable of estimating motor load and energy consumption within an average error of 10%. This margin is acceptable for most applications, considering the difference in cost involved in the two solutions and the ease of installation of the smart sensor. Thus, this paper demonstrates that the wireless IoT smart sensors are a promising tool for estimating load and energy consumption of electric motors. These estimates can contribute to reduce energy consumption and greenhouse gases emissions, positively impacting sustainability.