1 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada.
2 Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan.
3 Department of Computer Science, 6 G Research Center, Khalifa University, Abu Dhabi, United Arab Emirates.
4 Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, Canada.
5 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
6 Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland.
7 Research Center of Performance and Productivity Analysis, Istinye University, Sariyer/Istanbul, Turkey.
8 David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada.

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR), marking a paradigm shift from conventional, manually driven resuscitation practices to intelligent, data-driven interventions. It examines the evolution of CPR through the lens of predictive modeling, AI-enhanced devices, and real-time decision-making tools that collectively aim to improve resuscitation outcomes and survival rates. Unlike prior surveys that either focus solely on traditional CPR methods or offer general insights into ML applications in healthcare, this work provides a novel interdisciplinary synthesis tailored specifically to the domain of CPR. It presents a comprehensive taxonomy that classifies ML techniques into four key CPR-related tasks: rhythm analysis, outcome prediction, non-invasive blood pressure and chest compression modeling, and real-time detection of pulse and Return of Spontaneous Circulation (ROSC). The paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings. Furthermore, it highlights the role of eXplainable AI (XAI) in fostering clinical trust and adoption. By bridging the gap between resuscitation science and advanced ML techniques, this survey establishes a structured foundation for future research and practical innovation in ML-enhanced CPR. It offers clear insights, identifies unexplored opportunities, and sets a forward-looking research agenda identifying emerging trends and practical implementation challenges aiming to improve both the reliability and effectiveness of CPR in real-world emergencies.