In this study, we conducted a comparative evaluation of an enhanced Mahalanobis–Taguchi system (MTS) and convolutional neural networks (CNNs) for the recognition of acoustic signals, concentrating on the diagnostic monitoring of automotive window motors. The enhanced MTS integrates sophisticated feature selection and optimized Mahalanobis distance computations to improve anomaly detection using a specified sound quality index. High-resolution acoustic data were acquired utilizing dual high-sensitivity microphones to ensure reliable input for both methodologies. The CNN framework, enhanced by Long Short-Term Memory units and multiscale feature extraction, attained an accuracy greater than 96.6%. This performance notably exceeded that of the MTS, particularly in modeling intricate acoustic signal variations. Although the MTS provides statistical precision, it demonstrates reduced efficacy in managing subtle variability as opposed to adaptive CNN-based models. In this research, we elucidated the essential trade-offs between conventional and machine learning approaches, providing insights for choosing optimal acoustic monitoring methodologies within automotive applications.