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Hand gesture recognition using electromyography (EMG) signals has gained significant attention in recent years due to its potential applications in human-computer interaction, assistive technologies, and rehabilitation. EMG signals, which capture electrical activity produced by muscle contractions, provide valuable information for detecting and classifying hand movements. Various machine learning and deep learning techniques have been employed to improve the accuracy and efficiency of gesture recognition systems. This review explores the latest advancements in EMG-based hand gesture recognition, highlighting different signal acquisition methods, feature extraction techniques, classification algorithms, and challenges associated with real-time implementation. While promising progress has been made, issues such as signal variability, noise interference, and user dependency remain challenges that require further research. The integration of artificial intelligence and wearable technologies is expected to drive future innovations in this field.