Cardiac arrest continues to be a major cause of sudden death across the globe, and thus there is a need for designing efficient, real-time detection methods to improve patient outcomes. Based on the analysis of physiological signals, primarily data and vital parameters like blood pressure, heart rate, and others, the current study outlines the design and implementation of a machine learning-based system for the early diagnosis of cardiac arrest episodes. In order to extract clinically significant characteristics including heart rate variability (HRV), QRS complex shape, and statistical descriptors in both the time and frequency domains, the proposed system employs advanced preprocessing techniques, including signal denoising and normalization. Publicly accessible and anonymised clinical information were used to train and evaluate a variety of supervised learning algorithms. With the use of cross-validation and criteria the models’ performance was extensively examined. The system is intended to be integrated with bed or wearable monitoring devices to provide real-time alerting and continuous, non-invasive surveillance to medical staff. The results highlight the capabilities of machine learning in complementing conventional monitoring systems and advance the development of intelligent, data-driven strategies in critical care. Clinical validation, model personalization, and minimization of computational overhead will be pursued in future work for deployment in resource-limited settings.