<p>Heart disease (HD) is a leading cause of death globally, posing a significant healthcare burden. Early and correct diagnosis is crucial for effective management and improved patient outcomes. Machine learning (ML) has emerged as a promising tool for developing decision support systems to aid HD detection. This systematic review examined the current landscape of ML-based HD diagnostic systems, focusing on the utilized techniques, performance metrics, validation approaches, and publicly available datasets. The authors identified key research gaps, including data heterogeneity, class imbalance, lack of real-world validation, and limited integration of multi-modal data. Additionally, the authors discussed challenges related to model interpretability, ethical considerations, and the need for personalized medicine approaches. Finally, the authors explored promising future directions, such as the use of quantum machine learning and dynamic prediction systems for continuous monitoring. This comprehensive review presented valuable insights for researchers and healthcare professionals aiming to leverage the power of ML for improved HD diagnosis and patient care.</p>