In the context of structural analysis and design, natural frequencies play a vital role, and their prediction is essential in machine and vehicle design processes. The simulations related to the modal parameters are computationally intensive for systems with large complexity. This paper demonstrates on an illustrative academic example that natural frequencies can be successfully predicted using ML models. This paper aims to develop a model based on machine learning (ML) to predict a simple cantilever’s natural frequencies based on the physical parameters of the beam. The independent variables X are the geometric parameters including width, length, and thickness, while the dependent variable Y is the natural frequency. The study is framed using a systematic methodology that covers the stages of data collection, ML model selection, model training and validation. The validation process proves the effectiveness of ML as a computationally cheap replacement for traditional methods of prediction. The current research contributes to the investigation of the usage of commercially available ML tools in structural engineering. We report that the ODYSSEE A-Eye software is capable of natural frequency prediction with a varying geometry structure with less than 4% error for an 80-member training set of cantilever beam with various dimensions. Further developments will include considerations of noise, vibration, and harshness (NVH) to enhance system performance and improve user comfort.