In a treatment plant, the processes are complex ones, such as wastewater pH neutralization, which is very difficult to model, to control, being challenging to predict the treated water pH value at the exit of the process because of the process dynamic and non-linear behavior and because of the disturbances that occur. The identification of any method (AI-based) that can be used for wastewater pH prediction (knowing the acid and basic type reactants stream flowrates, their concentrations and initial pH) is a desideratum to ensure a neutral pH for the plant effluent, with tangible benefits on the plant emissary. Deep Learning (DL) and Machine Learning (ML) techniques provide optimal solutions that can be explored to find the best tool for the addressed problem. In the present paper, were customized and analyzed twelve Python algorithms, respectively six ML regression algorithms and six DL ones, implemented using Python 3.9 software and TensorFlow library. After a presentation of the neutralization process and a short description of the analyzed algorithms (custom Python ML and DL), the tailored ML and DL models were compared using specific evaluation metrics, with a number of five C# pre-implemented algorithms, in order to identify that ML or DL method (custom or pre-implemented) that is more suitable for an accurate wastewater pH prediction. Also, for the same purpose, it is proposed a mathematical model based on the method that was identified to be as the most appropriate.