Heart disease remains a leading cause of mortality worldwide, emphasizing the need for accurate and early prediction methods. This research paper proposes a novel hybrid machine learning approach, integrating fuzzy logic and machine learning, to predict heart risk at an early stage. Fuzzy logic provides a robust framework to handle uncertainties and vagueness in medical data, while machine learning models excel in pattern recognition and predictive accuracy. Combining these methodologies, we aim to develop a system that enhances prediction accuracy and offers interpretable and actionable insights for medical professionals. Our experimental results demonstrate the efficacy of the proposed hybrid approach, highlighting its potential to improve early detection and intervention strategies for heart disease significantly.