Cardiovascular diseases continue to be among the major causes of death globally, hence making early diagnosis and monitoring crucial to enhance the quality of care. Systems that utilise electrocardiogram (ECG) signals together with artificial intelligence,e such as machine learning, deep learning, and the Internet of Things (IoT), together with wearable health devices, have revolutionised cardiac diagnostics in the contemporary age. In this literature review, there will be an extensive evaluation of new developments in ECG signal processing and arrhythmia detection techniques, wearable ECG monitors, and intelligent health applications. This work assesses different machine learning algorithms that include SVM, CNN, LSTM, MLP and hybrid deep learning algorithms that can be applied to classify ECG signals. Other areas that are covered include remote IoT healthcare systems, cloud computing based on ECG monitoring, explainable artificial intelligence models, FHIR interoperability standards and others. The strengths, limitations, data sets, pre-processing techniques, and results achieved by recent studies are reviewed.