Machine learning is making significant advancements in the medical field, particularly in the analysis and annotation of Electrocardiography (ECG) waveforms, which are crucial for diagnosing heart conditions such as arrhythmias and ischemia. Open-source frameworks and advanced tools like Deep-Learning Based ECG Annotation and MathWorks Waveform Segmentation use neural networks to improve the accuracy and efficiency of ECG interpretation, even though challenges remain in achieving perfect performance. Machine learning algorithms can analyze vast datasets to identify patterns and correlations in ECG data, facilitating early detection of heart conditions and personalized care for patients. Tools like Encord ECG, OHIF ECG Viewer, and WaveformECG provide various levels of functionality for ECG annotation, with features that cater to different user needs, from beginners to advanced researchers. These developments demonstrate the potential of AI in enhancing medical diagnosis and treatment, offering automated, faster, and more accurate analysis of heart health indicators.