Moving Average Filters are used for digital signal processing applications, such as smoothing ECG signals by minimizing high-frequency noise. This work investigates the design and implementation of Moving Average Filters (MAFs) based on Verilog HDL in iterative and recursive methods with fixed-point and floating-point arithmetic. The research compares the four different implementations—fixed-point iterative, fixed-point recursive, floating-point iterative, and floating-point recursive—tested in terms of performance such as resource usage, timing latency, power dissipation, and numerical precision. In the study, real-life ECG records obtained from the MIT-BIH Arrhythmia database are used to emulate the filter performance. Results prove that fixed-point recursive filters achieve the optimal trade-off between accuracy, stability, and hardware efficiency and therefore are suitable for real-time and low-power embedded applications. On the other hand, floating-point implementations provide increased dynamic range but in exchange for increased resource usage and sensitivity to rounding errors. This study aids in choosing the filter design for performance-limited signal processing applications.