Muscle atrophy occurs not only after prolonged postoperative recovery but also as a natural consequence of aging, with it being especially common in individuals with low early-life physical activity. Early-stage muscle loss is often overlooked, and the condition may already be severe by the time clinical signs emerge. Current muscle quality assessments substantially rely on physicians’ subjective judgment, and objective measures of such quality are lacking. To address this limitation, in this study, we developed a myophonogram measurement system integrated with deep learning algorithms for the noninvasive assessment of muscle quality. This system captures low-frequency myophonogram signals (time-domain signals) from the target muscle, applies the short-time Fourier transform (STFT) to these signals to obtain spectrograms (time–frequency-domain signals), and then inputs these spectrograms to six deep learning models (LeNet-5, AlexNet, VGG-16, ResNet-50, GoogLeNet, and DenseNet-121) to classify muscle quality as healthy or unhealthy. The system can complete the entire process from signal capture to muscle quality detection within 60 s. Each model used in this system achieved an accuracy above 90% (compared with the evaluations of expert clinicians), demonstrating that the system is an intelligent, rapid, and objective tool for the convenient and reliable self-monitoring of muscle health.