CVD remains the most common cause of morbidity and mortality around the world, and therefore there is an important need for having appropriate and accurate diagnostic modalities that could help improve outcomes for such patients. Traditional diagnostic methods, though fundamental in clinical practice, often meet the challenge of limitations in sensitivity, specificity, and scalability, especially in the detection of subtle or early signs of disease. Machine Learning has recently been considered a transformative tool, allowing powerful data-driven techniques in analyzing large, heterogeneous datasets—imaging, clinical records, genomic data—that would otherwise enable a more tailored, precise, and efficient diagnostics. This narrative review provides an overview of the application of machine learning in the diagnosis of cardiovascular diseases, detailing capabilities and advantages of various cardiac imaging modalities enhanced by machine learning, such as echocardiography, magnetic resonance imaging, and computed tomography. This review also covers the commonly used machine learning algorithms in cardiovascular disease diagnostics and elaborates on specific applications in a range of cardiovascular diseases, including coronary artery disease, heart failure, and arrhythmias. We also discuss major challenges and limitations of the clinical integration of ML in CVD, including data quality issues, lack of model interpretability, and alignment with clinical workflows. This review concludes by highlighting the promising role that ML has in advancing cardiovascular diagnostics and calling for further research, standardization, and collaboration to ensure safe and effective implementation in clinical settings.