Machine learning, a significant part of AI, plays a vital role in daily life, supporting tasks like decision-making and real-world interactions. This technology’s ability to analyze data and make predictions has value across fields such as agriculture, healthcare, and finance. In this research, we address a key healthcare challenge heart disease, which has become a prevalent health threat, often without symptoms. To tackle this, we propose using machine learning to forecast heart disease risk by analyzing medical data, including age, blood pressure, and cholesterol levels. Early identification of high-risk individuals through predictive modeling can facilitate preventive healthcare and potentially lower heart disease incidence. The proposed model includes data processing, feature selection, training, and accuracy evaluation using precision, recall, and F1-Score as metrics.