Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learning (DL) methods. However, these approaches often face challenges such as limited real-time applicability, non-representative test conditions, and the need for large datasets to achieve optimal performance. This paper presents a transfer-learning framework for knee joint angle prediction that requires only a few gait cycles from new subjects. Three datasets - Georgia Tech, the University of California Irvine (UCI), and the Sharif Mechatronic Lab Exoskeleton (SMLE) - containing four EMG channels relevant to knee motion were utilized. A lightweight attention-based CNN-LSTM model was developed and pre-trained on the Georgia Tech dataset, then transferred to the UCI and SMLE datasets. The proposed model achieved Normalized Mean Absolute Errors (NMAE) of 6.8 percent and 13.7 percent for one-step and 50-step predictions on abnormal subjects using EMG inputs alone. Incorporating historical knee angles reduced the NMAE to 3.1 percent and 3.5 percent for normal subjects, and to 2.8 percent and 7.5 percent for abnormal subjects. When further adapted to the SMLE exoskeleton with EMG, kinematic, and interaction force inputs, the model achieved 1.09 percent and 3.1 percent NMAE for one- and 50-step predictions, respectively. These results demonstrate robust performance and strong generalization for both short- and long-term rehabilitation scenarios.