In this paper, the damage mechanisms and residual strength prediction models of unidirectional glass-fiber reinforced polymers are investigated by acoustic emission(AE) technique. The material exhibits three damage modes: matrix cracking, fiber fracture, and interface damage. A novel AE descriptor, amplitude/centroid frequency (ACF), is introduced to differentiate interface damage from other damage modes. Moreover, three signal types exhibit a strong clustering effect when correlated with ACF and average frequency. Microscopic damage mechanisms of the samples are observed using scanning electron microscopy and correlated with AE signals. The AE signals are analyzed using machine learning, and the clustering analysis results are used as a training set to obtain classification models using support vector machine (SVM) and K-nearest neighbor (KNN) methods. Leveraging the traditional mechanical regression analysis prediction model, the study achieves prediction of the material’s residual strength post-fatigue through improvement in AE cumulative counting. Additionally, optimization of prediction results can be achieved by a certain kind of signal after clustering. The combination of supervised learning and residual strength prediction models can realize the real-time classification of AE signals and apply them to the prediction of residual strength, which has a significant application value in real-time monitoring.