Introduction: Surface electromyography (sEMG) signals are a key modality for gait phase recognition, as they reflect neuromuscular activation and capture gait dynamics essential for natural control in rehabilitation robotics and human–machine systems. However, reliable phase recognition under complex locomotion conditions remains challenging due to high inter-subject variability, ambiguous phase boundaries, and increased biomechanical diversity across gait types. Objective: To address this issue, this paper proposes a hybrid deep learning framework that leverages the complementary strengths of convolutional neural networks (CNNs) for local time-series feature extraction and bidirectional long short-term memory networks (BiLSTMs) for modeling bidirectional long-range dependencies. Methods: A dataset was collected from 11 healthy subjects performing five representative locomotion scenarios, with gait phases annotated using synchronized motion capture and plantar pressure signals. Results: The proposed method achieved an average classification accuracy of 95.81%, with performance variation across gait types within 4%. The corresponding averaged precision, recall, and F1-score were 94.35%, 95.66%, and 94.95%, respectively, indicating stable and well-balanced recognition performance across multiple locomotion scenarios. Notably, the framework maintained robust performance in biomechanically challenging conditions such as slope ascent and stair ambulation, demonstrating its effectiveness under complex gait dynamics. Conclusion: Overall, these results demonstrate that the proposed framework provides a stable and generalizable solution for sEMG-based gait phase recognition in complex locomotion environments.