Abstract: Cardiovascular disease remains the preeminent cause in healthcare monitoring. CVD has evolved into a major public health burden on a global scale. In 2025, it is estimated that over 20.5 million people will die from cardiovascular disease related conditions, a number that has surged by 60% over last 30 years. "Reducing the risk of cardiac arrest is a paramount clinical concern." This paper presents a comprehensive review on cardiovascular disease using machine learning techniques and substantiate out of popular dataset like Cleveland dataset, kaggle, UCI etc.
Methods: In this paper the researchers used different methods for detecting cardio vascular disease articles are extracted from Google scholar, Research Gate, Scopus search engines between 2022 to 2025.The research findings are present below for better understanding. Result: The review synthesis the advantage and limitation of different methodologies of researchers and dataset used for validation. Conclusion: Machine intelligence offers a viable alternative to conventional human led diagnostics for proactive screening. "Even though significant improvements have been made in this field, the lack of uniformity in prediction models has created a need for new and better solutions".

Keywords: heart disease, Artificial Intelligence, machine learning algorithm, heart disease dataset.

Downloads: | DOI: 10.17148/IARJSET.2026.13312