Cardiovascular disease remains one of the most important health challenges worldwide, contributing substantially to the rising death rates each year. In 2021, cardiovascular diseases (CVDs) were responsible for approximately 20.5 million deaths, representing 32% of all global fatalities. Among these 85% of fatalities were accounted for due to strokes and heart attacks, demonstrating the alarming severity of cardiovascular diseases. However, the twenty-first century has led to some serious advancements in terms of technology that can be leveraged further to advance medical sciences making it more effective and accurate. Emerging technologies, such as artificial intelligence (AI) and machine learning (ML), serve as important tools in making life more convenient for humans. However, alongside these advancements, several ailments have arisen, one of which is the silent heart attack—a condition that poses unique challenges due to its subtle symptoms. Despite having medical treatments for this condition, if medication or treatment is not provided at the proper time it can also prove to be fatal. But there are only a few approaches that are capable of forecasting the silent heart. This study analyses various technologies and methodologies, and based on that conducts comprehensive research mainly focusing on the path toward predicting and detecting heart disease using methods like artificial neural networks. Additionally, this research examines the role of key parameters such as age, sex, and cholesterol levels, in predicting the probability of encountering a silent heart attack.