This research study analyzes the heart conditions through ECG data, which has become crucial in medical diagnosis, particularly for detecting and classifying arrhythmias. Accurate identification of these irregularities enables timely intervention and treatment. Advances in signal processing and Artificial Intelligenec (AI) have transformed this field, leading to the development of automated detection and classification systems that support healthcare professionals in diagnosing heart conditions more efficiently. The system processes ECG signals to extract features indicative of arrhythmias. Machine-learning algorithms such as Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) are then used to classify these features into various arrhythmia categories. By training on extensive datasets of labeled ECG recordings, these algorithms can accurately identify patterns associated with specific heart conditions. However, developing these systems poses challenges, such as ensuring robustness and accuracy across diverse patient populations and recording conditions. Interpretability is also crucial, as healthcare professionals need to understand and trust the decisions made by these automated systems. Despite these challenges, signal processing and AI advancements are promising for enhancing arrhythmia detection and patient outcomes. By utilizing these technologies, greater efficiency and accuracy in diagnosing and managing heart disease can be achieved.