Despite advances in reperfusion therapy, ST-segment elevation myocardial infarction remains a leading cause of mortality in the general population. In this work, we present a multicenter multimodal dataset designed for AI-based 12-month prognostic stratification after STEMI. The cohort includes 822 patients with predischarge post-PCI (Percutaneous Coronary Intervention) 12-lead ECGs and comprehensive clinical, laboratory, echocardiographic, angiographic and pharmacological data, along with standardized acquisition and 12-month follow-up for major adverse cardiovascular events (MACE). Data were anonymized and harmonized, and a computer-vision pipeline has been designed to detect lead regions and extract analyzable signals. To our knowledge, unlike other existing public datasets, this is the first multicenter dataset coupling predischarge ECGs with rich multimodal context and longitudinal outcomes in STEMI, enabling robust AI models for personalized risk prediction. This approach helps to bridge the gap between acute diagnosis and long-term risk stratification, with significant potential to improve clinical decision-making and patient outcomes.