Accurate and real-time gait phase detection has become a major challenge in current intelligent prosthetic systems. The main reasons for this are latency limitations, changes in sensor data, and the lack of effective temporal modeling in existing approaches. Traditional methods such as CNN, LSTM, and Transformer based architectures face problems such as high computational complexity, delayed inference, or limited compatibility on edge devices. To overcome these limitations, in this research we proposed SwiftGaitNet, a low-latency Edge-AI framework that is specifically designed for real-time prosthetic gait phase detection. The proposed system consists of Force Sensitive Resistor (FSR) signals, Inertial Measurement Unit (IMU) data collected by an IoT based wearable sensing setup and foot pressure information coordinated by multimodal sensor fusion. Lightweight temporal attention, gated temporal convolution, adaptive residual fusion and efficient fully connected decision layers are used in this architecture, so that faster sequential learning is possible with less computational load. Compared to existing methods, the proposed model is specially optimized for edge inference, thus providing fast and reliable real-time predictions without the need for heavy processing. According to the results of the experimental evaluation, SwiftGaitNet demonstrated better performance than the traditional baseline models CNN, LSTM, Transformer and CNN-LSTM. This model achieved 97.6% accuracy, 97.6% precision, 97.6% recall, and 97.6% F1-score, evaluated using a subject-independent, 5-fold Leave-One-Subject-Out (LOSO) cross-validation protocol on a dataset of 2250 temporal windows collected from 50 walking trials across four gait-phase classes (Heel-Strike, Mid-Stance, Toe-Off, and Swing). Inference latency, averaging 12.1 ± 1.8 ms, was measured directly on the deployed ESP32 DevKit hardware using TensorFlow Lite Micro rather than estimated offline. The proposed framework demonstrates computational efficiency while maintaining accurate real-time gait-phase detection. These results suggest that SwiftGaitNet is a promising candidate for low-latency, edge-deployed gait phase detection in wearable and prosthetic sensing systems; however, further work involving closed-loop prosthetic-control integration and validation with prosthetic users is needed before its suitability for clinical or real-time prosthetic-assist deployment can be established.