Carotid Artery Disease (CAD) is a significant vascular disorder associated with ischemic stroke and vascular cognitive impairment (VCI), generally progressing asymptomatically before the emergence of clinical manifestations. Early detection of cognitive decline in CAD patients is essential for implementing preventive strategies. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) yield precise results; however, they remain costly and challenging to obtain for extensive screening. Carotid sonography, on the other hand, is a cheap, portable, and non-invasive way to check the thickness of the intima media (IMT), the shape of the plaque, and the speeds of the flow. Recent improvements in artificial intelligence (AI) have made it possible to automatically analyze these sonographic biomarkers, which makes it possible to predict cognitive risk early. This survey combines modern literature on deep learning architectures like Convolutional Neural Networks (CNN), U-Net, attention-based models, and hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) frameworks with traditional machine learning methods like Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). Comparative assessments demonstrate that multimodal AI models, which combine vascular imaging with neurocognitive metrics, exhibit superior predictive performance. Some of the problems that still need to be solved are small datasets, differences in acquisition, limited multimodal fusion, and a lack of Explainable Artificial Intelligence (XAI) frameworks. Future research ought to concentrate on standardized multicenter datasets, interpretable AI pipelines, and cloud-based telemedicine systems. AI-enabled sonography thus signifies a promising, cost-effective model for the proactive identification of vascular cognitive impairment.