Cardiovascular health concerns are a rapidly increasing integrated subject that involves the analysis along with retrieval of data from cardiac mechanisms to detect and treat cardiovascular illnesses as promptly as possible. AI technologies, notably deep learning, and machine learning methodologies, are more impactful and powerful tools for boosting an application's capabilities, and they are being used to analyze and identify disorders using medical data. This article provides a comprehensive evaluation of intelligence-based statistical modeling, as well as the possibilities of strong AI techniques that may be crucial in creating smart and improved systems for applications in the real world. The paper presents an overview of powered by artificial intelligence modeling, which may be applied in a range of domains. In biomedical applications, an electrocardiogram (ECG) is used to measure cardiac activity. Wearable technology, such as smartwatches and bands, can be used to constantly track ECGs and aid in the early diagnosis of cardiovascular issues. Each approach's proficiency is connected to ECG classification techniques that have been evaluated for accuracy, sensitivity, and the ECG signal is impacted by noise, which might degrade it and result in incorrect treatment. Preprocessing removed noise from the data, enabling the cardiac state to be predicted. The P motion, QRS sound, and T waveform feature extraction were located and completed.