Acute vestibular syndrome (AVS), marked by continuous vertigo, spontaneous nystagmus, and postural instability, presents a diagnostic challenge in emergency settings due to symptom overlap between benign peripheral disorders and central causes such as ischemic stroke. While bedside protocols like HINTS (Head Impulse, Nystagmus, Test of Skew) are effective, their accuracy is examiner-dependent. Video-oculography (VOG) has emerged as a transformative tool by providing objective, quantitative assessments of oculomotor function, including head impulse test metrics, nystagmus characteristics, and skew deviation. VOG-enhanced HINTS (vHINTS) has demonstrated superior diagnostic accuracy compared to traditional bedside evaluations and even early MRI in certain cases. This chapter highlights the growing role of artificial intelligence (AI) and machine learning in automating VOG interpretation. AI models, particularly those using time-series analysis and neural networks, can accurately classify strokes from raw vHIT data, rivaling conventional vestibulo-ocular reflex (VOR) gain analysis. Federated learning frameworks now enable collaborative model training across institutions while preserving patient privacy. VOG’s integration into telemedicine further expands access to specialist-level diagnostics in remote and resource-limited settings.