Healthcare is undergoing a transformation thanks to the combination of Internet of Things (IoT) and machine learning (ML) technologies, particularly in the monitoring and detection of heart problems. The results of recent studies that use IoT and ML to improve cardiac health monitoring and predictive diagnoses are summarized in this review paper. The combination of machine learning (ML) with the Internet of Things (IoT) provides a promising path towards personalized treatment, early disease identification, and better cardiac disease management. Through a thorough assessment of the literature, including research that make use of wearable sensors, cloud-based data analytics, and deep learning algorithms, this paper illustrates the potential of IoT and ML to revolutionize cardiac healthcare. The advantages of remote monitoring systems—which offer ongoing health data, facilitating pre-emptive actions and lessening the need for in-person visits—are covered. This paper also discusses security concerns, data privacy, and the incorporation of these technologies into current healthcare systems. This paper highlights the usefulness of IoT and ML in detecting heart disease, improving patient outcomes, and opening the door for a more effective, economical healthcare system by looking at case studies and recent research findings. Future research directions are discussed in the paper’s conclusion, with a focus on the necessity of scalable, secure, and patient-centred solutions in the continuing development of technology for heart disease monitoring and diagnostics.