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Background and Motivation – cardiovascular diseases are among the leading causes of death worldwide. Heart sound analysis, a vital component of cardiac diagnosis, has traditionally relied on manual auscultation using stethoscopes. Recent advancements in sensor technologies have enabled more precise, automated, and accessible cardiac monitoring. Objectives – This paper aims to review the current landscape of sensor technologies used for heart sound acquisition and analysis, focusing on their working principles, signal processing techniques, applications, and integration with artificial intelligence. Methods – The review categorizes heart sound sensors into acoustic, piezoelectric, MEMS-based, and AI-integrated types. It outlines their signal processing pipelines, including preprocessing, feature extraction, and classification. Comparisons are drawn based on key parameters such as sensitivity, power efficiency, and diagnostic potential. Various application scenarios including clinical diagnostics, remote monitoring, and biometric authentication are explored. Results – Emerging trends show a shift toward miniaturized, low-power, AI-compatible sensors like MEMS and smart stethoscopes. AI-integrated systems demonstrate improved accuracy in anomaly detection and classification of heart murmurs. Wearable sensors and smartphone-based auscultation are gaining prominence in telemedicine. Conclusion – Sensor-based heart sound analysis is poised to revolutionize cardiac diagnostics with enhanced portability, automation, and diagnostic accuracy. Future directions include sensor fusion, edge computing, and smart textiles to enable seamless, real-time cardiovascular monitoring.