Cardiovascular disorders continue to be a major global health issue, with arrhythmias presenting significant hurdles in both diagnosis and treatment. This article presents a groundbreaking and thorough framework for ECG feature assessment that incorporates morphological, temporal, and frequency-domain elements, all improved by advanced processing techniques and smart classification methods. By utilizing diverse features and tailoring approaches to individual patients, the proposed system enhances diagnostic accuracy and dependability. Evaluations on standard datasets indicate improved classification efficacy, marking a substantial advancement in automated systems for arrhythmia diagnosis. The framework’s adaptability further positions it as a strong prospect for integration into mobile and telemedicine platforms, thus facilitating diagnostic processes to near real-time clinical application.