Electrocardiogram (ECG) signal processing plays a pivotal role in diagnosing and monitoring cardiovascular diseases. With the advent of advanced signal processing techniques, the accuracy and reliability of ECG signal analysis have significantly improved. This paper comprehensively studies cutting-edge signal processing methodologies applied to ECG signals, including noise reduction, feature extraction, and classification algorithms. The focus is on techniques such as adaptive filtering (AF), Fourier transform (FT), and decision tree (DT) algorithms that enhance the detection and interpretation of cardiac abnormalities. An adaptive notch filter (ANF) is used in adaptive filtering, whereas a Fractional Fourier transform (FrFT) is used in the Fourier transform. The effectiveness of these advanced techniques is evaluated through extensive simulations and real-world data analysis, showcasing their potential to revolutionize clinical practices in cardiology. The findings underscore the importance of integrating advanced signal processing methods in ECG analysis to improve diagnostic accuracy, reduce false positives, and ultimately contribute to better patient outcomes.