The electrorefiner (ER) is the heart of pyroprocessing technology operating at a high-temperature (723 K – 773 K) to separate uranium from Experimental Breeder Reactor-II (EBR-II) used metallic fuel. One of the most common electroanalytical methods for determining the thermodynamic and electrochemical behavior of elemental species in the eutectic molten salt LiCl-KCl inside ER is cyclic voltammetry (CV). Information from CV can possibly be used to estimate diffusion coefficients, apparent standard potentials, transfer coefficients, and numbers of electron transferred. Therefore, predicting the trace of each species from the CV method in an absence of experimental data is important for safeguarding this technology. This work focused on the development an interactive computational design for the CV method by analyzing available uranium chloride data sets (1 to 10 wt%) in a LiCl-KCl molten salt at 773 K under different scan rates to help elucidating, improving, and providing robustness in detection analysis. A principle method and a computational code have been developed by using electrochemical fundamentals and coupling various variables such as: the diffusion coefficients, formal potentials, and process time duration. Although this developed computational model works moderately well with reported uranium data sets, it experiences difficulty in tracing zirconium data sets due to their complex CV structures. Therefore, an artificial neural intelligent (ANI) data analysis has been proposed to resolve this issue and to provide comparative study to the precursor computational modeling development. For this purpose, ANI has been applied on 0.5 to 5 wt% of zirconium chloride in LiCl-KCl eutectic molten salt at 773 K under different scan rates to mimic the system and provide current and potential simulated data sets for the unseen data. In addition, a Graphical User Interface (GUI) through the commercial software Matlab was created to provide a controllable environment for different users. The computational code shows a limitation in high concentration CV prediction, capturing the adsorption peaks, and provides a dissimilarity. However, the model is able to capture the important anodic and cathodic peaks of uranium chloride CV which is the main focus of this study. Furthermore, the developed code is able to calculate the concentration of each species as a function of time. Due to the complexity of the CV of zirconium chloride, the computational model is used to predict the probability reactions occurring at each peak. The resulting study reveals that the reaction at the highest anodic peak is related to the combination of 70% Zr/Zr+4 and 30% Zr/Zr+2 for the 1.07 wt% and 2.49 wt% zirconium chloride and 30% Zr/Zr+4 and 70% Zr/Zr+2 combination for 4.98 wt% ZrCl4. The proposed alternative ANI method has demonstrated its capability in predicting the trend of species in a new situation with a high accuracy on predictions without any dissimilarity. Two final structures from zirconium chloride study which high accuracy (that is, a low error) are related to [9, 15, 10]-18 and [10, 11, 25]-19. These two final structures have been applied on uranium chloride salt experimental data sets to further validate the ANI’s ability and concept. Three different fixed data combinations were considered. The result indicates that by increasing the number of training data sets it does not necessarily help improving the prediction process. ANI implementation outcome on uranium chloride data set illustrates a good prediction with a specific fixed data combination and [9, 15, 10]-18 structure. Thus, it can be concluded that ANI is a promising method for safeguarding pyroprocessing technology due to its robustness in predicting the CV plots with high accuracy.