This study focuses on analyzing electrocardiogram (ECG) records related to sudden cardiac death in youth and analyze the factors influencing a sudden cardiac arrest. Sudden cardiac arrest poses a severe threat to life and can result in death if not addressed promptly. Recent American research indicates a 13% increase in sudden cardiac arrest cases among individuals aged between 35–45. Furthermore, the Indian Heart Association has reported that heart disease tends to affect Indians at a younger age compared to other demographic groups, often without prior warning. Studies reveal that Indians tend to experience heart diseases approximately a decade earlier than their Western counterparts. This work aims to conduct data collection and surveys to collect ECG records and other relevant information that is crucial for training the machine learning models to achieve the study outcomes. The study's scope encompasses evaluating the feasibility of the task, identifying relevant datasets, conducting data preprocessing, and assessing the performance using K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and random forest algorithm. Among these, both SVM and Random Forest demonstrated superior performance, with the Random Forest algorithm further fine-tuned to align with the dataset's specific requirements. The refined model exhibited 93% accuracy.