<![CDATA[. In recent years, heart and cardiovascular diseases have become more common, causing a significant increase in mortality rates worldwide. Data from various organisations highlights the severity of heart disease, which remains a major concern. Accurately and quickly identifying severe conditions, such as heart disease, is vital for effective prevention. Techniques such as data mining, machine learning, and deep learning have been used in medicine to reliably detect heart disease. However, these methods depend on data that can change over time. To ensure accurate detection, proper use of historical data is essential; otherwise, results can be inaccurate. Machine learning techniques produce outcomes based on mathematical calculations, so data cleaning and refinement are necessary. Disease-related data can include text, numbers, and images, which may vary widely, requiring extensive stratification, normalisation, cleaning, encoding, and randomisation; otherwise, results may be biased. Our previous review article addressed a specific challenge related to the CVD Prediction Model. This Enhanced review primarily examines how machine learning techniques operate on medical datasets and their effectiveness in predicting cardiovascular diseases (CVD). It also aims to analyse datasets, features, and machine learning methods used in CVD prediction]]>
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