Cardiovascular diseases (CVDs) remain one of the leading causes of mortality around the globe, highlighting the need for more effective tools that enable early diagnosis and continuous heart sound monitoring. While the traditional stethoscope has served healthcare for generations, it offers little in terms of digital connectivity or remote diagnostic capability. In our work, we introduce a Bluetooth‑enabled stethoscope supported by AI‑driven anomaly detection, designed to make cardiac assessments both more accessible and more precise. The system records heart sounds through a condenser microphone, processes them with an ESP‑32 microcontroller, and transmits the cleaned signals wirelessly. Processed data is securely stored in a MySQL database and presented in real time through a mobile application. A 1D Convolutional Neural Network (CNN), trained on publicly available datasets, classifies heart sounds as normal or abnormal. Early tests suggest our low-cost, portable system has promising potential to bridge the divide between traditional auscultation and modern telemedicine.