The Electrocardiogram (ECG) is a critical clinical record for diagnosing Arrhythmia and elucidating various cardiovascular diseases. Achieving a clear morphology and denoised signal is pivotal for physicians. In this context, our paper provides a comparative study of filtering techniques tailored for ECG signal enhancement. We investigate three distinct kinds of filters: Linear, non-linear, and adaptive filters. A comparative analysis is conducted, considering key performance metrics such as Signal-to-Noise Ratio (SNR) and MSE. The study underscores that the Adaptive Median filter, with a parameter of 5, overtakes others, highlighting its adaptability in handling dynamic ECG signal characteristics and contributing to exceptional noise reduction and signal fidelity. The outcomes furnish valuable insights into the strengths and limitations of each filtering approach, assisting researchers and practitioners in selecting the most suitable technique for their specific ECG signal processing needs.