<p>Cardiovascular disease (CVD) is a predominant global health concern responsible for 31% of deaths worldwide. Standard detection techniques for the disease are Electrocardiogram (ECG), Chest Radiography, laboratory investigation such as Cardiac Troponin, Coronary Angiography, and Echocardiography. Patients endure diverse natural illness symptoms. CVD patients need rapid and correct medical monitoring to survive. Therefore, CVD prediction is crucial to allow healthcare practitioners to mitigate risks and give early treatment. Artificial Intelligence (AI) and Machine Learning (ML) models have been widely used in CVD detection and have yielded promising results in numerous studies. However, most of these studies are “black box” approaches that strive for explainability, transparency, and interpretability, identifying the importance of the factors that contribute to CVD, thus depriving practitioners of the ability to develop targeted treatment strategies. The present study, therefore, discusses the role of Explainable Artificial Intelligence (XAI) in CVD prediction and addresses the challenges outlined above. At the outset, XAI’s various terminologies are described, highlighting its potential, and then the multiple concepts used in developing the XAI framework are elucidated. This study presents an exhaustive review of XAI applications, emphasizing their use in various CVD datasets. The review of studies justifies the potential advantages of XAI-based frameworks in ensuring accurate and confident decision-making, which has further channelized appropriate treatment strategies for healthcare providers. The critical analysis of the studies also reveals the challenges associated with the implementation of XAI that direct the potential future direction of research.</p>