Purpose: Carotid artery interventions carry risk of complications including stroke, myocardial infarction and death. Careful patient selection with identification of high-risk individuals is crucial to operative planning. Artificial intelligence (AI) claims to allow prediction of patient outcomes by analysis of preoperative data leading to improved patient selection. This review assesses the evidence for use of artificial intelligence in risk stratification for carotid endarterectomy and carotid artery stenting. Methodology: Pubmed, Web of Knowledge, EMBASE, and the Cochrane library were systematically searched to identify articles utilising artificial intelligence to predict surgical outcomes in carotid endarterectomy or carotid artery stenting. After duplicate removal, all studies underwent title and abstract screening followed by quality assessment. Data extraction was then carried out for synthesis and comparison of study outcomes. Results: Eleven clinical studies were identified after article screening. AI was found to have accurate predictive ability for 30-day mortality (AUC 0.73, 95% CI 0.61-0.79), postoperative 30-day major adverse cardiovascular event (AUC 0.91, 95% CI 0.90-0.92) and postoperative 1-year stroke (AUC 0.90, 95% CI 0.89-0.91) or death (AUC 0.96, 95% CI 0.95-0.97). AI was also used to predict intraoperative shunt requirement (AUC 0.87, 95% CI 0.83-0.92). The results of these studies identify the potential of AI to be utilised in selecting high-risk patients and prognostication of postoperative outcomes. However, many studies were limited by small patient populations and lack of external validation. Conclusion This systematic review highlights the potential application of artificial intelligence in prediction of surgical outcomes in carotid artery intervention with outperformance of current clinical algorithms. However, use of these tools in a clinical setting requires further robust study with use of external validation and larger patient datasets.