Bioimpedance spectrum (BIS) measurements are highly appreciated in in vivo studies. This non-destructive method, supported by simple and efficient instrumentation, is widely used in clinical applications. The multi-frequency approach allows for the efficient extraction of the most information from the measured data. However, low-frequency implementations are still unexploited in the development of the technique. A self-developed BIS measurement technology is considered the pioneering approach for low (<5 kHz) and ultra-low (<100 Hz) frequency range studies. In this paper, the robustness of ultra-low frequency measurements in the prototypes is examined using specially constructed physical models and a dedicated neural network-based software. The physical models were designed to model the dispersion mainly in the ultra-low frequency range. The first set of models was used in the training of the software environment, while the second set allowed a complete verification of the technology. Further, the Hilbert transformation was employed to adjust the imaginary components of complex signals and for phase determination. The findings showed that the prototypes are capable of efficient and robust data acquisition, regardless of the applied frequency range, minimizing the impact of measurement errors. Consequently, in in vivo applications, these prototypes minimize the variance of the measurement results, allowing the resulting BIS data to provide a maximum representation of biological phenomena.