Whether tropical cyclones (TC) possess chaotic dynamics is an open question in current TC research. The existence of such chaotic dynamics is profound for TC model development and operational forecast, as it sets a limit on how much one can further improve intensity forecast skills or models in the future. Rapid advances of machine learning (ML) techniques and applications open up an opportunity to explore TC intensity chaos from a different angle. Building upon our recent results on the low-dimensional chaos of TC intensity, this study presents a novel use of ML models to quantify TC intensity chaos. By treating TC scales as input features for ML models, we show that TC intensity displays a limited predictability range of ~3 hours due to chaotic variability at the potential intensity (PI) equilibrium. This short predictability range for TC intensity is robust across ML architectures including deep neural networks (DNN), gated recurrent units (GRU), and long-short term memory (LSTM) examined in this study. Using the minimum central pressure as a metric for TC intensity could extend the predictability range to 5-6 hours, yet the limited predictability for TC intensity is still well captured in all ML models. As a result, the intrinsic variability of TC intensity related to low-dimensional chaos prevents intensity errors in any TC model from being arbitrarily reduced, regardless of how perfect a TC model or vortex initialization is. Our findings support the existence of chaotic dynamics at the PI limit and demonstrate an innovative way of applying ML to study atmospheric predictability.