Signal and data processing are necessary for analyzing experimental data. Signal processing techniques are well-defined and handy for real-life applications, but applying those techniques to experimental data is challenging for the researcher. In structural health monitoring, post-processing helps remove noise and residuals from the sensor data. Various software and programming can help achieve the best signal-processing algorithms. Each package has its solver to solve the signal processing algorithms. Due to the countless options available for signal processing, choosing the best and most reliable package to solve the problem is foremost. Nowadays, MATLAB and Python are two packages that are reliable, popular, and accurate for multiple applications. This article evaluates the signal processing capabilities of MATLAB and Python in the context of structural health monitoring applications. File handling, simulating the signals, filter realization, filter response, and adding and removing noise from the signal are discussed and implemented for structural health monitoring. Signal processing is implemented, keeping structural health monitoring as the focus area. The real-life demonstration used a supported beam instrumented with Fiber Bragg Grating strain sensors. It has been determined that both MATLAB and Python can be utilized for structural health monitoring based on cross-correlation techniques, as the absolute difference between their results is approximately 0.0001, which is negligible.