<p>Integrated phase-based vibration sensing and communication faces challenges in achieving high-sensitivity detection under cost-effective commercial external-cavity lasers (ECLs), primarily limited by laser phase noise and frequency offset drift. To address this, we propose a machine-learning-enhanced phase-based vibration sensing scheme for coherent digital subcarrier multiplexing (DSCM) systems. In this scheme, a generative adversarial network (GAN)-based digital twin (DT) system is developed to synthesize massive forward-phase sensing datasets, overcoming experimental data scarcity. Principal component analysis (PCA) confirms high consistency between generated and experimental noise characteristics. The generated datasets are used to train a multi-layer perceptron (MLP) and an attention-enhanced MLP (AMLP). The trained artificial neural networks (ANNs) are deployed in receiver-side digital signal processing (DSP) for vibration-induced phase extraction. In experimental validation under 10 kHz vibration, the MLP-based scheme achieves a 13 dB sensing signal-to-noise ratio (SSNR) enhancement over the traditional band-pass filter (BPF) scheme. The AMLP-based scheme further delivers a 7 dB additional SSNR gain by leveraging attention mechanisms to emphasize critical phase features. Furthermore, the ANNs-based scheme successfully extracts multi-type vibrations without prior vibration information, demonstrating the flexibility of the ANNs-based schemes. This work establishes a machine-learning-driven framework that enhances phase-based sensing in commercial ECLs-based DSCM systems, providing a viable pathway for intelligent optical network operation and maintenance.</p>