The integration of machine learning solutions is becoming more prominent in the industry. In industrial maintenance, new approaches categorized under predictive maintenance primarily use machine learning to identify patterns that could lead to machine failures. However, in most cases, implementing a machine learning approach is very expensive regarding resources and experienced personnel. Therefore, this approach is usually more costly in some machines than replacing these faulty machinesinstead. This paper proposes a low-cost machine-learning approach to detect anomalies in a rotary machine by monitoring its casing temperatureusing EdgeImpulse to Train the model and a Raspberry Pico as the microcontroller. The project is divided into two phases.Data is collected to be used to train and test the model. The model is then deployed to themicrocontroller andisconnected toasensor attached to the motor. The model developed showed promisingresults with an accuracy of 91% and a ƒ1 score of 0.91.