This paper introduces a new AI-based Audio-Visual Speech Enhancement (AVSE) system and presents a comparative performance analysis of different deployment architectures. The proposed AVSE system employs convolutional neural networks (CNNs) for spectral feature extraction and long short-term memory (LSTM) networks for temporal modeling, enabling robust speech enhancement through multimodal fusion of audio and visual cues. Multiple deployment scenarios are investigated, including cloud-based, edge-assisted, and standalone device implementations. Their performance is evaluated in terms of speech quality improvement, latency, and computational overhead. Real-world experiments are conducted across various network conditions, including Ethernet, Wi-Fi, 4G, and 5G, to analyze the trade-offs between processing delay, communication latency, and perceptual speech quality. The results show that while cloud deployment achieves the highest enhancement quality, edge-assisted architectures offer the best balance between latency and intelligibility, meeting real-time requirements under 5G and Wi-Fi 6 conditions. These findings provide practical guidelines for selecting and optimizing AVSE deployment architectures in diverse applications, including assistive hearing devices, telepresence, and industrial communications.

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