Cardiac arrest remains a significant global health concern, demanding accurate and timely prediction methods to enhance patient outcomes. In this research paper, we present a comparative study demonstrating the importance of convolutional neural networks (CNNs) over artificial neural networks (ANNs) in the prediction of cardiac arrest. Leveraging a comprehensive dataset of physiological signals and medical imaging, we systematically analyze the performance of CNN and ANN models. Our results unequivocally highlight the superior predictive capabilities of CNNs, showcasing their ability to extract intricate spatial features and temporal patterns for more accurate and early cardiac arrest detection. This study sheds light on the transformative potential of CNNs in revolutionizing cardiac arrest prediction and improving patient care.