This paper introduces an innovative Electrocardiogram (ECG) feature extraction and reduction system designed to elevate cardiac analysis capabilities. By integrating cutting-edge multi-domain feature extraction techniques with dimensionality reduction methods, the system offers a comprehensive approach to cardiac data analysis. Notably, it combines advanced transform-based features, such as the Hilbert transform, Mel-frequency Cepstral Coefficients (MFCC), and Multiple Signal Classification (MUSIC) algorithm analyses, with a range of effective feature reduction techniques, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Multi-Dimensional Scaling (MDS), Uniform Manifold Approximation and Projection (UMAP), and autoencoders. Processing two-channel ECG recordings from the (MIT-BIH) arrhythmia database in 60-s intervals, the system extracts and condenses 59 diverse features, capturing subtle nuances and prominent cardiac characteristics. PCA and ICA techniques yielded 19 reduced features each, while other methods provided 15 reduced features. The dimensionality reduction techniques exhibited varied performance: PCA had a runtime of 1.21 s, explained variance of 0.98, reconstruction error of 0.0170, silhouette score of 0.2025; ICA had a runtime of 0.55 s, explained variance of 0.98, reconstruction error of 0.0170, silhouette score of 0.0654; MDS had a runtime of 38.84 s, a reconstruction error of 0.0548, silhouette score of 0.2052, and normalized stress of 0.021; UMAP had a runtime of 9.95 s, silhouette score of 0.3653, and normalized stress of 0.391; autoencoders had a runtime of 6.28 s, explained variance of 0.9258, reconstruction error of 0.0574, and silhouette score of 0.2657. These metrics provide insights into the efficiency and effectiveness of each technique in enhancing ECG data analysis and cardiac diagnostics.