Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification machine learning (ML) modelling. Four different ML models were developed; Models 1 (M1) and M2 based on NIR (100 inputs from 1596–2396 nm) and M3 and M4 based on the e-nose (nine sensor readings as inputs) and 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neuron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 98.9%, M2 = 98.3%, M3 = 96.8%, and M4 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.