Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating advancements in early disease detection and intervention strategies. Among the various manifestations of CVDs, arrhythmias pose significant challenges due to their potential life-threatening nature and the complexity of timely diagnosis. Traditional diagnostic methods often struggle to identify the subtle symptoms and intermittent occurrence of arrhythmias, highlighting the need for more advanced diagnostic tools. This paper presents a novel approach that uses convolutional neural networks (CNNs) to enhance the classification of arrhythmias from electrocardiogram (ECG) data. The proposed CNN model uses the intricate patterns in the data to accurately identify different types of arrhythmias. The dataset comes from the MIT-BIH Arrhythmia Database containing records for 48 patients ranging from 23 years to 89 years of age from Beth Israel Hospital Arrhythmia Laboratory between 1975 and 1979. Preprocessing steps such as noise filtering, normalization, and data augmentation were employed to improve model robustness. The CNN architecture, optimized for 1D time-series data, extracts hierarchical features that capture both the voltage signals and temporal patterns of arrhythmic beats achieving a model accuracy of 98.66%. Compared to standard diagnostic techniques, our approach demonstrates superior sensitivity and specificity, particularly in detecting critical events like premature ventricular contractions and atrial premature beats.