<![CDATA[Heart attacks are the leading cause of death globally due to delays in early detection. Wearable technology offers innovative solutions in real-time heart health monitoring through the measurement of heart rate (HR), heart rate variability (HRV), oxygen levels (SpO2), and electrocardiogram (ECG). This research analyzes the role of wearable devices in heart attack prediction as well as the challenges and opportunities for its development. The method used is Systematic Literature Review (SLR) and secondary data analysis from scientific sources and global health reports. The results show that the integration of artificial intelligence (AI) and machine learning (ML) improves detection accuracy, with models such as Random Forest, SVM, and Deep learning (CNN & LSTM) proving effective. However, challenges remain, such as sensor accuracy, data privacy, and societal adoption of the technology. The future of wearable technology in cardiology is promising with nanotechnology-based sensors, electronic medical record (EHR) integration, and the development of non-invasive sensors. If these challenges can be overcome, wearable devices have the potential to become a key tool in the prevention and early detection of heart attacks, supporting the transformation of health systems based on data and AI.]]>
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