Cardio-forge is a cutting-edge project that uses cutting-edge data science to enhance heart health. This project uses major health indicators to predict heart disease using machine learning models. Finding patterns and correlations in datasets allows us to learn important things about cardiovascular health. To create predictive models, we deploy algorithms such as random forest, decision tree, SVM and logistic regression. Our mission is to facilitate the early diagnosis and treatment of cardiovascular diseases. Childhood heart disease is a serious public health issue. Treatment effectiveness depends on early detection and accurate prediction. For evaluating heart health, noninvasive methods such as electrocardiograms (ECGs) and electrocardiograph measurements are crucial. The accuracy of diagnosis and prediction can be improved by standardizing Z-scores for electrocardiograph measurements and by gathering normal ECG data from a pediatric population that is diverse in terms of race. Cardio-forge seeks to provide healthcare professionals and the general public with practical insights that can be used to prevent and treat heart disease early on, ultimately saving lives all over the world.