Cardiac arrest is a critical medical emergency with potential life-threatening consequences. In this paper, machine learning techniques are used to develop a model for early identification of cardiac arrest for an individual. In this paper we are implementing machine learning and deep learning models like Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbour (KNN), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN). The main aim of the paper is to know how well the machine learning models are applicable for the prediction of cardiac arrest. Based on the factors accuracy, precision, recall and F1-score the performance will be calculated. These models analyse the health data to identify any uncovered signs that could signal a future cardiac arrest.