Adaptive filters, like the LMS algorithm, find extensive use in real-world applications such as biomedical signal enhancement and noise cancellation. This study compares adaptive filters like LMS, FLMS, NLMS, BLMS, and RLS for noise reduction in audio signals. The research emphasizes the impact of filter choice and parameters, particularly step size, on noise reduction. The study underscores the significance of optimal filter order in ALE and the critical role of block size optimization in BLMS for efficient noise reduction. The results indicate that the Adaptive filter outperforms BLMS in noise reduction, producing filtered signals closer to the original input. This research sheds light on implementing and comparing adaptive filters for noise reduction in audio signals, stressing the importance of optimal filter order and block size for effective noise reduction strategies.