Developed a comprehensive machine learning solution to analyze and predict heart attack risks by identifying critical predictors using advanced techniques (Logistic Regression, Random Forest, SVM, Gradient Boosting, Voting Classifier, and Neural Networks). Achieved top accuracy of 0.803 with both Gradient Boosting and Neural Network models, supporting early detection and preventive care initiatives. Conducted extensive exploratory data analysis, feature engineering, and model evaluation (using Accuracy, Confusion Matrices, ROC-AUC, etc.) while ensuring compliance with GDPR/DSGVO standards. Produced clear visualizations and detailed reports to effectively communicate risk factors and model outcomes, contributing to proactive healthcare solutions.