Irregular heartbeats known as arrhythmia can be detected by studying the ECG signals in which RR intervals are calculated to find out the beats per minute (BPM) (Yeh and Wang in Comput Methods Progr Biomed 91:245–254, 2008 [1]). These BPM numbers help in identifying eight different types of arrhythmias. Each phase represents different levels of irregularity in cardiac rhythm that assist doctors to diagnose and plan for treatment. Real-time monitoring with precise classification is made possible through machine learning methods together with advanced algorithms used by ECG analysis systems while dealing with arrhythmias. This aids in early intervention as well as personalized management approaches necessary to avert risks like congestive heart failure or stroke. There is promise for better outcome among patients with arrhythmia managed continuously due to improvements brought about by technological advancement in cardiac monitoring devices. Timely identification and intervention are required when it comes to dealing with arrhythmia. In this case, ECG signals measure RR intervals which determine the number of times the heart beats within one minute (BPM), and therefore, they can be grouped into eight stages of arrhythmias depending on their rate. However, a correct diagnosis needs care. Advancements in technology and healthcare systems are important for better diagnosis of arrhythmia and improving patient results since they help in early detection which leads to immediate medical attention, hence cutting down on complications such as heart failure or stroke.