The most prevalent cause of death worldwide is cardiovascular disease (CVD), which is responsible for approximately one-third of the overall deaths. Conventional methods of diagnosis—electrocardiographs (ECG), reflective of the heart (echocardiography), and imaging, despite being widespread, are largely limited due to inter-observer instability, poor sensitivity, and inefficiency during real-time diagnostics. The combination of Artificial Intelligence (AI) and deep learning (and high-depth machine learning) algorithms, especially, has led to a steep increase in the rate of diagnosis, accuracy, and scalability of heart diseases. The chapter describes the application of AI in the field of different diagnostic areas, like the Convolutional Neural Networks (CNNs) on cardiac imaging, the Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks on time-series analysis of ECGs, and the Support Vector Machines (SVMs) on structured clinical data. Empirical works indicate that AI-driven models have been able to boost the detection of myocardial infarction by 1520% more than benchmark rule-based algorithms and categorize arrhythmia with greater than 95% accuracy on huge ECG collections, including PhysioNet. AI instruments have been shown to predict atrial fibrillation and ventricular hypertrophy, and do so early on, in addition to showing robust generalization in different patient cohorts. As well as algorithm development, the chapter examines central issues of implementation, including the interpretability of models, data bias, and ethical transparency, and covers the blistering growth of AI-driven wearable and mobile health technology in the continuous detection of cardiac diseases. With the development of the field, explainable AI and federated learning stand to add even more to the democratization of access to high-quality diagnostics, whilst guaranteeing patient privacy and clinical credibility. In this chapter, the authors highlight the promise of AI in transforming cardiovascular diagnostics by delivering more targeted, predictive, and equitable care due to its well-designed and clinically proven applications.