OBJECTIVE: Current literature on patients with peripheral arterial disease (PAD) is heterogeneous and characterised by selective outcome reporting. A core outcome set (COS) is a list of essential outcomes based on expert consensus used to standardise clinical research, reflecting the needs of all stakeholders, including patients. This is especially important in research involving artificial intelligence (AI), such as the VASCUL-AID project, a European collaboration aimed at creating predictive models for cardiovascular disease progression in patients with chronic symptomatic PAD. This study aimed to create a COS for patients with chronic symptomatic PAD for use in clinical and AI research. METHODS: This COS was pre-registered in the Core Outcome Measures in Effectiveness Trials (COMET) database and was developed in line with the COMET Initiative recommendations. A longlist of PAD outcomes was formed based on outcomes identified from a systematic literature review and focus groups with patients and healthcare professionals (HCPs). Next, a Delphi study with patients and HCPs was conducted. The COS was finalised in an expert consensus meeting with key opinion leaders, patients, and patient representatives. RESULTS: The longlist comprised 67 outcomes: 36 outcomes identified from a systematic literature review and 31 patient centred outcomes from the focus groups. In total, 153 HCPs and 92 patients participated in the Delphi study, with an overall participation rate of 85%. The Delphi study's 20 highest rated outcomes were discussed in the expert consensus meeting, attended by 21 HCPs, three patients, and two patient representatives. The final COS consists of 11 outcomes: death, myocardial infarction, major amputation, acute limb ischaemia, thromboembolic complications, disease progression, pain free walking distance, physical activity, health related quality of life, smoking cessation, and good communication by healthcare provider. CONCLUSION: This is the first COS for patients with chronic symptomatic PAD recommended for adoption in clinical and AI research.