This paper presents an AI-based PHM (Prognostics and Health Management) framework for quantitative motor health assessment and remaining useful life (RUL) prediction. The proposed method first defines a health index using vibration and current signals of an industrial motor, and then adopts a two-stage PHM architecture consisting of health-state classification and deep learning-based RUL prediction. A degradation test bench is designed to obtain condition monitoring data for normal, warning, and critical states, and a hybrid 1D CNN–BiLSTM–attention model is developed to capture both local features and long-term temporal dependencies. Experimental results demonstrate that the proposed model outperforms conventional SVM and single LSTM baselines in terms of both health-state classification accuracy and RUL prediction accuracy, achieving a 20–30% reduction in RMSE and more than 80% of RUL predictions within ±10% error. The proposed approach provides a practical PHM framework and modeling guidelines for implementing condition-based maintenance of electric motors in smart manufacturing environments.